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\n  \n 2023\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n \n Breast-Density Semantic Segmentation with Probability Scaling for BI-RADS Assessment using DeepLabV3.\n \n \n \n \n\n\n \n Testagrose, C.; Gupta, V.; Erdal, B.; White, R.; Maxwell, R.; Liu, X.; Kahanda, I.; Elfayoumy, S.; Klostermeyer, W.; and Demirer, M.\n\n\n \n\n\n\n In Proceedings of the 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB), 2023. ACM (Full paper acceptance rate: 29%)\n \n\n\n\n
\n\n\n\n \n \n \"Breast-Density paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/bcb23/TestagroseEtAl,\n  author = {Conrad Testagrose and Vikash Gupta and Barbaros Erdal and Richard White and\n                                                Robert Maxwell and Xudong Liu and Indika Kahanda and Sherif Elfayoumy and\n            William Klostermeyer and Mutlu Demirer\n                                                },\n  booktitle = {Proceedings of the 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)},\n  publisher = {ACM (Full paper acceptance rate: <font color="red">29%</font>)},\n  url_Paper = {http://unfail.ccec.unf.edu/pubs.html},\n  title = {Breast-Density Semantic Segmentation with Probability Scaling for BI-RADS Assessment using DeepLabV3},\n  year = 2023\n}\n\n
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\n \n\n \n \n \n \n \n \n MarshCover: A Web-based Tool for Estimating Vegetation Coverage in Marsh Images Using Convolutional Neural Networks.\n \n \n \n \n\n\n \n Welch, L.; and Liu, X.\n\n\n \n\n\n\n In Proceedings of the 36th International Florida Artificial Intelligence Research Society Conference (FLAIRS), 2023. Florida Online Journals (Full paper acceptance rate: 38%)\n \n\n\n\n
\n\n\n\n \n \n \"MarshCover: paper\n  \n \n \n \"MarshCover:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 13 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/flairs36/WelchL,\n  author = {Lucas Welch and Xudong Liu},\n  booktitle = {Proceedings of the 36th International Florida Artificial Intelligence Research Society Conference (FLAIRS)},\n  publisher = {Florida Online Journals (Full paper acceptance rate: <font color="red">38%</font>)},\n  title = {MarshCover: A Web-based Tool for Estimating Vegetation Coverage in Marsh Images Using Convolutional Neural Networks},\n  url_Paper = {http://unfail.ccec.unf.edu/resources/MarshCover_flairs36.pdf},\n  url = {https://journals.flvc.org/FLAIRS/article/view/133166},\n  year = 2023\n}\n\n
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\n \n\n \n \n \n \n \n \n Comparative Study Between Vision Transformer and EfficientNet on Marsh Grass Classification.\n \n \n \n \n\n\n \n Testagrose*, C.; Shabbir*, M.; Weaver*, B.; and Liu, X.\n\n\n \n\n\n\n In Proceedings of the 36th International Florida Artificial Intelligence Research Society Conference (FLAIRS), 2023. Florida Online Journals (Full paper acceptance rate: 38%)\n \n\n\n\n
\n\n\n\n \n \n \"Comparative paper\n  \n \n \n \"ComparativePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/flairs36/TestagroseSWL,\n  author = {Conrad Testagrose* and Mehlam Shabbir* and Braden Weaver* and Xudong Liu},\n  booktitle = {Proceedings of the 36th International Florida Artificial Intelligence Research Society Conference (FLAIRS)},\n  publisher = {Florida Online Journals (Full paper acceptance rate: <font color="red">38%</font>)},\n  title = {Comparative Study Between Vision Transformer and EfficientNet on Marsh Grass Classification},\n  url_Paper = {http://unfail.ccec.unf.edu/resources/ComparativeMarsh_flairs36.pdf},\n  url = {https://journals.flvc.org/FLAIRS/article/view/133132},\n  year = 2023\n}\n\n
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\n \n\n \n \n \n \n \n \n Heart Murmur Classification in Phonocardiogram Representations Using Convolutional Neural Networks.\n \n \n \n \n\n\n \n Shabbir*, M.; Liu, X.; Nasseri, M.; and Helgeson, S.\n\n\n \n\n\n\n In Proceedings of the 36th International Florida Artificial Intelligence Research Society Conference (FLAIRS), 2023. Florida Online Journals\n \n\n\n\n
\n\n\n\n \n \n \"Heart paper\n  \n \n \n \"HeartPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/flairs36/ShabbirLNH,\n  author = {Mehlam Shabbir* and Xudong Liu and Mona Nasseri and Scott Helgeson},\n  booktitle = {Proceedings of the 36th International Florida Artificial Intelligence Research Society Conference (FLAIRS)},\n  publisher = {Florida Online Journals},\n  title = {Heart Murmur Classification in Phonocardiogram Representations Using Convolutional Neural Networks},\n  url_Paper = {http://unfail.ccec.unf.edu/resources/Murur_flairs36.pdf},\n  url = {https://journals.flvc.org/FLAIRS/article/view/133189},\n  year = 2023\n}\n\n
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\n \n\n \n \n \n \n \n \n BioFactCheck: Exploring the Feasibility of Explainable Automated Inconsistency Detection in Biomedical and Health.\n \n \n \n \n\n\n \n Lamichhane*, P.; Kahanda, I.; Liu, X.; Umapathy, K.; Reddivari, S.; Christie, C.; Arikawa, A.; and Ross, J.\n\n\n \n\n\n\n In Proceedings of the IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2023. IEEE\n \n\n\n\n
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@inproceedings{conf/chase23/LamichhaneKLURCAR,\n  author = {Prajwol Lamichhane* and Indika Kahanda and Xudong Liu and Karthik Umapathy and Sandeep Reddivari and Catherine Christie and Andrea Arikawa and Jen Ross},\n  booktitle = {Proceedings of the IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)},\n  publisher = {IEEE},\n  title = {BioFactCheck: Exploring the Feasibility of Explainable Automated Inconsistency Detection in Biomedical and Health},\n  url_Paper = {http://unfail.ccec.unf.edu/resources/BioFactCheck_chase23.pdf},\n  year = 2023\n}\n\n
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\n  \n 2022\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Impact of Concatenation of Digital Craniocaudal Mammography Images on a Deep-Learning Breast-Density Classifier Using Inception V3 and ViT.\n \n \n \n \n\n\n \n Testagrose, C.; Gupta, V.; Erdal, B.; White, R.; Maxwell, R.; Liu, X.; Kahanda, I.; Elfayoumy, S.; Klostermeyer, W.; and Demirer, M.\n\n\n \n\n\n\n In Proceedings of the International Conference on Bioinformatics and Biomedicine (BIBM), accepted and presented at the 6th BIBM International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics (DLB2H), 2022. IEEE Press\n \n\n\n\n
\n\n\n\n \n \n \"Impact paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 21 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wsh/dlb2h22/TestagroseEtAl,\n  author = {Conrad Testagrose and Vikash Gupta and Barbaros Erdal and Richard White and\n            Robert Maxwell and Xudong Liu and Indika Kahanda and Sherif Elfayoumy and \n            William Klostermeyer and Mutlu Demirer\n            },\n  booktitle = {Proceedings of the International Conference on Bioinformatics and Biomedicine (BIBM), accepted and presented at the 6th BIBM International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics (DLB2H)},\n  publisher = {IEEE Press},\n  url_Paper = {http://unfail.ccec.unf.edu/resources/Mammo_dlb2h22.pdf},\n  title = {Impact of Concatenation of Digital Craniocaudal Mammography Images on a Deep-Learning Breast-Density Classifier Using Inception V3 and ViT},\n  abstract = {\nBreast density is an indicator of a patient’s predisposed risk of breast\ncancer. Although not fully understood, increased breast density increases the\nlikelihood of developing breast cancer. Accurate assessment of breast\ndensity from mammogram images is a challenging task for the radiologist. A\npatient’s breast densityis assigned to one of four categories outlined by\nBreast Imaging and Reporting Data Systems (BI-RADS). There have been efforts to\nidentify automated approachesto assist radiologists in the classification\nof a patient’s breast density. The interest in using deep learning to fulfill\nthis need for an automated approachhas seen a significant increase in\nrecent years. The preprocessing techniques used to develop these deep learning\napproaches often have a profound impact on the model’s accuracy and\nclinical viability. In this paper, we outline a novel image preprocessing\ntechnique where we concatenate individual mammogram images and compare the\nresults using this technique between Inception-v3 and a vision transformer\n(ViT). The results are compared using the area under (AUC) the receiver op\nerator characteristics (ROC) curves and traditional accuracy metrics.\n},\n  year = 2022\n}\n\n
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\n Breast density is an indicator of a patient’s predisposed risk of breast cancer. Although not fully understood, increased breast density increases the likelihood of developing breast cancer. Accurate assessment of breast density from mammogram images is a challenging task for the radiologist. A patient’s breast densityis assigned to one of four categories outlined by Breast Imaging and Reporting Data Systems (BI-RADS). There have been efforts to identify automated approachesto assist radiologists in the classification of a patient’s breast density. The interest in using deep learning to fulfill this need for an automated approachhas seen a significant increase in recent years. The preprocessing techniques used to develop these deep learning approaches often have a profound impact on the model’s accuracy and clinical viability. In this paper, we outline a novel image preprocessing technique where we concatenate individual mammogram images and compare the results using this technique between Inception-v3 and a vision transformer (ViT). The results are compared using the area under (AUC) the receiver op erator characteristics (ROC) curves and traditional accuracy metrics. \n
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\n \n\n \n \n \n \n \n \n Recycling Material Classification using Convolutional Neural Networks.\n \n \n \n \n\n\n \n Liu, K.; and Liu, X.\n\n\n \n\n\n\n In Proceedings of the 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022. IEEE Press (Accepted, Full paper acceptance rate: 32.4%)\n \n\n\n\n
\n\n\n\n \n \n \"RecyclingPaper\n  \n \n \n \"Recycling paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 33 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/icmla22/LiuL,\n  author = {Kaihua Liu and Xudong Liu},\n  booktitle = {Proceedings of the 21st IEEE International Conference on Machine Learning and Applications (ICMLA)},\n  publisher = {IEEE Press (Accepted, Full paper acceptance rate: <font color="red">32.4%</font>)},\n  url = {https://www.icmla-conference.org/icmla22/},\n  abstract = {Using convolutional neural networks for classifying recyclable materials has shown promises for an effective and efficient way to classify recyclable trash. This work aims to demonstrate the most accurate CNN architecture for this task on our dataset combined from multiple sources, where in total 12,873 images of recyclable materials are collected over four classes: glass, metal, paper, and plastic. To this end, we use this dataset to train the CNN models, including a simple 8-layer CNN, AlexNet, VGGNet and InceptionNet are compared. Our empirical results show that VGGNet combined with transfer learning produces the best testing accuracy of 84.6\\%. Furthermore, we import this best model to a Raspberry Pi application and an Android application to demonstrate the potential forconsumer and industrial usage.\n},\n  url_Paper = {http://unfail.ccec.unf.edu/resources/Recycle_icmla22.pdf},\n  title = {Recycling Material Classification using Convolutional Neural Networks},\n  year = 2022\n}\n\n
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\n Using convolutional neural networks for classifying recyclable materials has shown promises for an effective and efficient way to classify recyclable trash. This work aims to demonstrate the most accurate CNN architecture for this task on our dataset combined from multiple sources, where in total 12,873 images of recyclable materials are collected over four classes: glass, metal, paper, and plastic. To this end, we use this dataset to train the CNN models, including a simple 8-layer CNN, AlexNet, VGGNet and InceptionNet are compared. Our empirical results show that VGGNet combined with transfer learning produces the best testing accuracy of 84.6%. Furthermore, we import this best model to a Raspberry Pi application and an Android application to demonstrate the potential forconsumer and industrial usage. \n
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\n  \n 2021\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n \n On Learning Probabilistic Partial Lexicographic Preference Trees.\n \n \n \n \n\n\n \n Liu, X.\n\n\n \n\n\n\n In Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021. IEEE Press\n \n\n\n\n
\n\n\n\n \n \n \"On paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 34 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/icmla21/Liu,\n  author = {Xudong Liu},\n  booktitle = {Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)},\n  publisher = {IEEE Press},\n  abstract = {Proposed by Liu and Truszczynski [1], partial lexicographic preference trees, PLP-trees, for short, are intuitive and predictive data structures used to model qualitative user preferences over combinatorial domains. In this work, we introduce uncertainty into PLP-trees to propose probabilistic partial lexicographic preference trees, or PPLP-trees. We define such formalism, where uncertainty exhibits in the probability distributions on selecting both the next important feature throughout the model and the preferred value in the domain of every feature. We then define semantics of PPLP-trees in terms of the probability of some object strictly preferred over another object, the probability of some object equivalent with another object, and the probability of some object being optimal. We show that these probabilities can be computed in time polynomial in the size of the tree. To this end, we study the problem of passive learning of PPLP-trees from user examples and demonstrate our learning algorithm, a polynomial time greedy heuristic, bound by a branching factor throughout the construction of the tree.\n},\n  url_Paper = {http://unfail.ccec.unf.edu/resources/PPLPT_icmla21.pdf},\n  title = {On Learning Probabilistic Partial Lexicographic Preference Trees},\n  year = 2021\n}\n\n
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\n Proposed by Liu and Truszczynski [1], partial lexicographic preference trees, PLP-trees, for short, are intuitive and predictive data structures used to model qualitative user preferences over combinatorial domains. In this work, we introduce uncertainty into PLP-trees to propose probabilistic partial lexicographic preference trees, or PPLP-trees. We define such formalism, where uncertainty exhibits in the probability distributions on selecting both the next important feature throughout the model and the preferred value in the domain of every feature. We then define semantics of PPLP-trees in terms of the probability of some object strictly preferred over another object, the probability of some object equivalent with another object, and the probability of some object being optimal. We show that these probabilities can be computed in time polynomial in the size of the tree. To this end, we study the problem of passive learning of PPLP-trees from user examples and demonstrate our learning algorithm, a polynomial time greedy heuristic, bound by a branching factor throughout the construction of the tree. \n
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\n \n\n \n \n \n \n \n \n Probabilistic Lexicographic Preference Trees.\n \n \n \n \n\n\n \n Liu, X.; and Truszczynski, M.\n\n\n \n\n\n\n In Proceedings of the 7th International Conference on Algorithmic Decision Theory (ADT), 2021. Springer\n \n\n\n\n
\n\n\n\n \n \n \"Probabilistic paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 12 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/adt21/LiuT,\n  author = {Xudong Liu and Miroslaw Truszczynski},\n  booktitle = {Proceedings of the 7th International Conference on Algorithmic Decision Theory (ADT)},\n  publisher = {Springer},\n  title = {Probabilistic Lexicographic Preference Trees},\n  abstract = {We introduce probabilistic lexicographic preference trees (or PrLPTs for short). We show that they offer intuitive and often compact representations of non-deterministic qualitative preferences over alternatives in multi-attribute (or, combinatorial) binary domains. We specify how a PrLPT defines the probability that a given outcome has a given rank, and the probability that a given outcome is preferred to another one, and show how to compute these probabilities in polynomial time. We also show that computing outcomes that are optimal with the probability equal to or exceeding a given threshold for some classes of PrLP-trees is in P, but for some other classes the problem is NP-hard.},\n  url_Paper = {http://unfail.ccec.unf.edu/resources/PLPT_adt21.pdf},\n  year = 2021\n}\n\n
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\n We introduce probabilistic lexicographic preference trees (or PrLPTs for short). We show that they offer intuitive and often compact representations of non-deterministic qualitative preferences over alternatives in multi-attribute (or, combinatorial) binary domains. We specify how a PrLPT defines the probability that a given outcome has a given rank, and the probability that a given outcome is preferred to another one, and show how to compute these probabilities in polynomial time. We also show that computing outcomes that are optimal with the probability equal to or exceeding a given threshold for some classes of PrLP-trees is in P, but for some other classes the problem is NP-hard.\n
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\n \n\n \n \n \n \n \n Vegetation Coverage in Marsh Grass Photography Using Convolutional Neural Networks.\n \n \n \n\n\n \n Welch*, L.; Liu, X.; Reddivari, S.; Umapathy, K.; and Kahanda, I.\n\n\n \n\n\n\n In Proceedings of the 34th International Florida Artificial Intelligence Research Society Conference (FLAIRS), 2021. AAAI Press\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 42 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/flairs34/WelchLRUK,\n  author = {Lucas Welch* and Xudong Liu and Sandeep Reddivari and Karthikeyan Umapathy and Indika Kahanda},\n  booktitle = {Proceedings of the 34th International Florida Artificial Intelligence Research Society Conference (FLAIRS)},\n  publisher = {AAAI Press},\n  title = {Vegetation Coverage in Marsh Grass Photography Using Convolutional Neural Networks},\n\tabstract = {Vegetation monitoring is one of the major cornerstones of environmental protection today, giving scientists a look into changing ecosystems. One important task in vegetation monitoring is to estimate the coverage of vegetation in an area of marsh. This task often calls for extensive human labor carefully examining pixels in photos of marsh sites, a very time-consuming process. In this paper, aiming to automate this process, we propose a novel framework for such automation using deep neural networks. Then, we focus on the utmost component to build convolutional neural networks (CNNs) to identify the presence or absence of vegetation. To this end, we collect a new dataset with the help of Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) to be used to train and test the effectiveness of our selected CNN models, including LeNet-5 and two variants of AlexNet. Our experiments show that the AlexNet variants achieves higher accuracy scores on the test set than LeNet-5, with 92.41\\% for a AlexNet variant on distinguishing between vegetation and the lack thereof. These promising results suggest us to confidently move forward with not only expanding our dataset, but also developing models to determine multiple species in addition to the presence of live vegetation.},\n\t%url="",\n  %url_Paper = {http://unfail.ccec.unf.edu/resources/veg_aaai21.pdf},\n  year = 2021\n}\n\n
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\n Vegetation monitoring is one of the major cornerstones of environmental protection today, giving scientists a look into changing ecosystems. One important task in vegetation monitoring is to estimate the coverage of vegetation in an area of marsh. This task often calls for extensive human labor carefully examining pixels in photos of marsh sites, a very time-consuming process. In this paper, aiming to automate this process, we propose a novel framework for such automation using deep neural networks. Then, we focus on the utmost component to build convolutional neural networks (CNNs) to identify the presence or absence of vegetation. To this end, we collect a new dataset with the help of Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) to be used to train and test the effectiveness of our selected CNN models, including LeNet-5 and two variants of AlexNet. Our experiments show that the AlexNet variants achieves higher accuracy scores on the test set than LeNet-5, with 92.41% for a AlexNet variant on distinguishing between vegetation and the lack thereof. These promising results suggest us to confidently move forward with not only expanding our dataset, but also developing models to determine multiple species in addition to the presence of live vegetation.\n
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\n \n\n \n \n \n \n \n \n Measuring Vegetation Density in Marsh Grass Photographs Using Deep Neural Networks (Student Abstract).\n \n \n \n \n\n\n \n Welch*, L.; and Liu, X.\n\n\n \n\n\n\n In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021. AAAI Press\n \n\n\n\n
\n\n\n\n \n \n \"Measuring paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 32 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/aaai21/WelchL,\n  author = {Lucas Welch* and Xudong Liu},\n  booktitle = {Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI)},\n  publisher = {AAAI Press},\n  title = {Measuring Vegetation Density in Marsh Grass Photographs Using Deep Neural Networks (Student Abstract)},\n\tabstract = {To protect the world’s marshlands, it is of utmost importance to be able to monitor their vegetation composition and coverage. This currently is accomplished by large teams of researchers and volunteers manually looking at the marsh images and labeling randomly selected pixels by what species (or lack thereof) is present at the pixel. This task, however, is extremely labor intensive, limiting the amount of quality environmental monitoring that can be done in the field. If the task was automated, teams would be able to monitor larger swaths of land. In this paper, we propose a novel framework for such automation using deep neural networks. Then, we focus on the key component of this framework: a binary classifier to decide whether a pixel is vegetated or not. To this end, we create a dataset of labeled snippet images out of publicly available photoquadrats of the marshlands in Florida. Finally, we construct LeNet-5 and AlexNet, adjusted to our input snippets, faster training time, networks and experiment to learn them on our dataset for the binary classification task. Our results show that the AlexNet model achieves higher accuracy on the test set than the LeNet-5 model, with 92.41\\% for AlexNet and 91.34\\% for LeNet-5.},\n\t%url="",\n  url_Paper = {http://unfail.ccec.unf.edu/resources/veg_aaai21.pdf},\n  year = 2021\n}\n\n
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\n To protect the world’s marshlands, it is of utmost importance to be able to monitor their vegetation composition and coverage. This currently is accomplished by large teams of researchers and volunteers manually looking at the marsh images and labeling randomly selected pixels by what species (or lack thereof) is present at the pixel. This task, however, is extremely labor intensive, limiting the amount of quality environmental monitoring that can be done in the field. If the task was automated, teams would be able to monitor larger swaths of land. In this paper, we propose a novel framework for such automation using deep neural networks. Then, we focus on the key component of this framework: a binary classifier to decide whether a pixel is vegetated or not. To this end, we create a dataset of labeled snippet images out of publicly available photoquadrats of the marshlands in Florida. Finally, we construct LeNet-5 and AlexNet, adjusted to our input snippets, faster training time, networks and experiment to learn them on our dataset for the binary classification task. Our results show that the AlexNet model achieves higher accuracy on the test set than the LeNet-5 model, with 92.41% for AlexNet and 91.34% for LeNet-5.\n
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\n \n\n \n \n \n \n \n \n Clustering Partial Lexicographic Preference Trees (Student Abstract).\n \n \n \n \n\n\n \n Allen*, J.; Liu, X.; Reddivari, S.; and Umapathy, K.\n\n\n \n\n\n\n In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021. AAAI Press\n \n\n\n\n
\n\n\n\n \n \n \"Clustering paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 18 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/aaai21/AllenLRU,\n  author = {Joseph Allen* and Xudong Liu and Sandeep Reddivari and Karthikeyan Umapathy},\n  booktitle = {Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI)},\n  publisher = {AAAI Press},\n  title = {Clustering Partial Lexicographic Preference Trees (Student Abstract)},\n\tabstract = {In this work, we consider distance-based clustering of partial lexicographic preference trees (PLP-trees), intuitive and compact graphical representations of user preferences over multi-valued attributes. To compute distances between PLP-trees, we propose a polynomial time algorithm that computes Kendall’s τ distance directly from the trees and show its efficacy compared to the brute-force algorithm. To this end, we implement several clustering methods (i.e., spectral clustering, affinity propagation, and agglomerative nesting) augmented by our distance algorithm, experiment with clustering of up to 10,000 PLP-trees, and show the effectiveness of the clustering methods and visualizations of their results.},\n\t%url="",\n  url_Paper = {http://unfail.ccec.unf.edu/resources/PLPClustering_aaai21.pdf},\n  year = 2021\n}\n\n
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\n In this work, we consider distance-based clustering of partial lexicographic preference trees (PLP-trees), intuitive and compact graphical representations of user preferences over multi-valued attributes. To compute distances between PLP-trees, we propose a polynomial time algorithm that computes Kendall’s τ distance directly from the trees and show its efficacy compared to the brute-force algorithm. To this end, we implement several clustering methods (i.e., spectral clustering, affinity propagation, and agglomerative nesting) augmented by our distance algorithm, experiment with clustering of up to 10,000 PLP-trees, and show the effectiveness of the clustering methods and visualizations of their results.\n
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\n  \n 2020\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Multivariate Probability Calibration with Isotonic Bernstein Polynomials.\n \n \n \n \n\n\n \n Wang*, Y.; and Liu, X.\n\n\n \n\n\n\n In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI), 2020. IJCAI\n \n\n\n\n
\n\n\n\n \n \n \"MultivariatePaper\n  \n \n \n \"Multivariate paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 24 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/ijcai20/WangL,\n  author = {Yongqiao Wang* and Xudong Liu},\n  booktitle = {Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI)},\n  publisher = {IJCAI},\n  title = {Multivariate Probability Calibration with Isotonic Bernstein Polynomials},\n  abstract = {Multivariate probability calibration is the problem of predicting class membership probabilities from classification scores of multiple classifiers. To achieve better performance, the calibrating function is often required to be coordinate-wise non-decreasing; that is, for every classifier, the higher the score, the higher the probability of the class labeling being positive. To this end, we propose a multivariate regression method based on shape-restricted Bernstein polynomials. This method is universally flexible: it can approximate any continuous calibrating function with any specified error, as the polynomial degree increases to infinite. Moreover, it is universally consistent: the estimated calibrating function converges to any continuous calibrating function, as the training size increases to infinity. Our empirical study shows that the proposed method achieves better calibrating performance than benchmark methods.},\n  url="https://doi.org/10.24963/ijcai.2020/353",\n  url_Paper = {http://unfail.ccec.unf.edu/resources/probcal_ijcai2020.pdf},\n  year = 2020\n}\n\n
\n
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\n Multivariate probability calibration is the problem of predicting class membership probabilities from classification scores of multiple classifiers. To achieve better performance, the calibrating function is often required to be coordinate-wise non-decreasing; that is, for every classifier, the higher the score, the higher the probability of the class labeling being positive. To this end, we propose a multivariate regression method based on shape-restricted Bernstein polynomials. This method is universally flexible: it can approximate any continuous calibrating function with any specified error, as the polynomial degree increases to infinite. Moreover, it is universally consistent: the estimated calibrating function converges to any continuous calibrating function, as the training size increases to infinity. Our empirical study shows that the proposed method achieves better calibrating performance than benchmark methods.\n
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\n \n\n \n \n \n \n \n \n Clustering Partial Lexicographic Preference Trees.\n \n \n \n \n\n\n \n Allen, J.; Liu, X.; Reddivari, S.; and Umapathy, K.\n\n\n \n\n\n\n In Proceedings of the 33rd International Florida Artificial Intelligence Research Society Conference (FLAIRS), 2020. AAAI Press\n \n\n\n\n
\n\n\n\n \n \n \"ClusteringPaper\n  \n \n \n \"Clustering paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 12 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/flairs33/AllenLRU,\n  author = {Joseph Allen and Xudong Liu and Sandeep Reddivari and Karthikeyan Umapathy},\n  booktitle = {Proceedings of the 33rd International Florida Artificial Intelligence Research Society Conference (FLAIRS)},\n  publisher = {AAAI Press},\n  title = {Clustering Partial Lexicographic Preference Trees},\n  abstract = {In this work, we consider the problem of clustering partial lexicographic preference trees (PLP-trees), intuitive and often compact representations of user preferences over multi-valued attributes. Due to the preordering nature of PLP-trees, we define a variant of Kendall’s τ distance metric to be used to compute distances between PLP-trees for clustering. To this end, extending the previous work by Li and Kazimipour (Li and Kazimipour 2018), we propose a polynomial timealgorithm PlpDis to compute such distances, and present empirical results comparing it against the brute-force baseline. Based on PlpDis, we use various distance-based clustering methods to cluster PLP-trees learned from a car evaluation dataset. Our experiments show that hierarchical agglomerative nesting (AGNES) is the best choice for clustering PLP-trees, and that the single-linkage variant of AGNES is the best fit for clustering large numbers of trees.},\n\turl="https://aaai.org/ocs/index.php/FLAIRS/FLAIRS20/paper/view/18425",\n  url_Paper = {http://unfail.ccec.unf.edu/resources/PLPClustering_flairs20.pdf},\n  year = 2020\n}\n\n
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\n In this work, we consider the problem of clustering partial lexicographic preference trees (PLP-trees), intuitive and often compact representations of user preferences over multi-valued attributes. Due to the preordering nature of PLP-trees, we define a variant of Kendall’s τ distance metric to be used to compute distances between PLP-trees for clustering. To this end, extending the previous work by Li and Kazimipour (Li and Kazimipour 2018), we propose a polynomial timealgorithm PlpDis to compute such distances, and present empirical results comparing it against the brute-force baseline. Based on PlpDis, we use various distance-based clustering methods to cluster PLP-trees learned from a car evaluation dataset. Our experiments show that hierarchical agglomerative nesting (AGNES) is the best choice for clustering PLP-trees, and that the single-linkage variant of AGNES is the best fit for clustering large numbers of trees.\n
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\n  \n 2019\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n \n New Complexity Results on Aggregating Lexicographic Preferences Trees Using Positional Scoring Rules.\n \n \n \n \n\n\n \n Liu, X.; and Truszczynski, M.\n\n\n \n\n\n\n In Proceedings of the 6th International Conference on Algorithmic Decision Theory (ADT), 2019. Springer\n \n\n\n\n
\n\n\n\n \n \n \"NewPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 13 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/adt19/LiuT,\n  author = {Xudong Liu and Miroslaw Truszczynski},\n  booktitle = {Proceedings of the 6th International Conference on Algorithmic Decision Theory (ADT)},\n  publisher = {Springer},\n\turl="https://link.springer.com/chapter/10.1007/978-3-030-31489-7_7",\n  title = {New Complexity Results on Aggregating Lexicographic Preferences Trees Using Positional Scoring Rules},\n  abstract = {Aggregating votes that are preference orders over candidates or alternatives is a fundamental problem of decision theory and social choice. We study this problem in the setting when alternatives are described as tuples of values of attributes. The combinatorial spaces of such alternatives make explicit enumerations of alternatives from the most to the least preferred infeasible. Instead, votes may be specified implicitly in terms of some compact and intuitive preference representation mechanism. In our work, we assume that votes are given as lexicographic preference trees and consider two preference-aggregation problems, the winner problem and the evaluation problem. We study them under the assumption that positional scoring rules are used for aggregation. In particular, we consider k-Approval and b-Borda, a generalized Borda rule, and we discover new computational complexity results for them.},\n  year = 2019\n}\n\n
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\n Aggregating votes that are preference orders over candidates or alternatives is a fundamental problem of decision theory and social choice. We study this problem in the setting when alternatives are described as tuples of values of attributes. The combinatorial spaces of such alternatives make explicit enumerations of alternatives from the most to the least preferred infeasible. Instead, votes may be specified implicitly in terms of some compact and intuitive preference representation mechanism. In our work, we assume that votes are given as lexicographic preference trees and consider two preference-aggregation problems, the winner problem and the evaluation problem. We study them under the assumption that positional scoring rules are used for aggregation. In particular, we consider k-Approval and b-Borda, a generalized Borda rule, and we discover new computational complexity results for them.\n
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\n \n\n \n \n \n \n \n \n Voting-based Ensemble Learning for Partial Lexicographic Preference Forests over Combinatorial Domains.\n \n \n \n \n\n\n \n Liu, X.; and Truszczynski, M.\n\n\n \n\n\n\n Annals of Mathematics and Artificial Intelligence, Springer, 87: 137-155. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"Voting-basedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 14 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{j/AMAI19/LiuT,\n  author = {Xudong Liu and Miroslaw Truszczynski},\n  title = {Voting-based Ensemble Learning for Partial Lexicographic Preference Forests over Combinatorial Domains},\n  journal = {Annals of Mathematics and Artificial Intelligence, Springer},\n  volume    = {87}, \n  pages     = {137-155},\n  issue     = {1-2},\n  publisher = {Springer},\n  abstract = {We study preference representation models based on partial lexicographic preference trees\n(PLP-trees). We propose to represent preference relations as forests of small PLP-trees\n(PLP-forests), and to use voting rules to aggregate orders represented by the individual\ntrees into a single order to be taken as a model of the agent’s preference relation. We show\nthat when learned from examples, PLP-forests have better accuracy than single PLP-trees.\nWe also show that the choice of a voting rule does not have a major effect on the aggregated order, thus \nrendering the problem of selecting the “right” rule less critical. Next, for\nthe proposed PLP-forest preference models, we develop methods to compute optimal and\nnear-optimal outcomes, the tasks that appear difficult for some other common preference\nmodels. Lastly, we compare our models with those based on decision trees, which brings up\nquestions for future research.},\n\tissn="1573-7470",                                                               \n\tdoi="10.1007/s10472-019-09645-7",                                               \n\turl="https://doi.org/10.1007/s10472-019-09645-7",\n  year = 2019\n}\n\n
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\n We study preference representation models based on partial lexicographic preference trees (PLP-trees). We propose to represent preference relations as forests of small PLP-trees (PLP-forests), and to use voting rules to aggregate orders represented by the individual trees into a single order to be taken as a model of the agent’s preference relation. We show that when learned from examples, PLP-forests have better accuracy than single PLP-trees. We also show that the choice of a voting rule does not have a major effect on the aggregated order, thus rendering the problem of selecting the “right” rule less critical. Next, for the proposed PLP-forest preference models, we develop methods to compute optimal and near-optimal outcomes, the tasks that appear difficult for some other common preference models. Lastly, we compare our models with those based on decision trees, which brings up questions for future research.\n
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\n \n\n \n \n \n \n \n \n Hourly Weather Data Projection due to Climate Change for Impact Assessment on Building and Infrastructure.\n \n \n \n \n\n\n \n Jiang, A.; Liu, X.; Czarnecki, E.; and Zhang, C.\n\n\n \n\n\n\n Sustainable Cities and Society, Elsevier. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"HourlyPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{j/SCS19/JLiuCZ,\n  author = {Aiyin Jiang and Xudong Liu and Emily Czarnecki and Chengyi Zhang},\n  title = {Hourly Weather Data Projection due to Climate Change for Impact Assessment on Building and Infrastructure},\n  journal = {Sustainable Cities and Society, Elsevier},\n  publisher = {Elsevier},\n\tissn = "2210-6707",\n\tdoi = "https://doi.org/10.1016/j.scs.2019.101688",\n\turl = "http://www.sciencedirect.com/science/article/pii/S2210670719304810",\n\tabstract = {The global climate change research has been conducted for a few years in various professional communities. \nIn the building industry, researchers usually investigate the future building energy demands due to the climate change \nby simulation software. The input  les to the simulation software includes projected weather data and building models. \nAlthough there exist a few mathematical methods to project the future weather, the morphing method is the most well-known among them. \nIn the meantime, the simulation software and weather data are in a variety of formats depending on country of origin and/or simulation package. \nIn order to provide both the research and the professional communities the possibility to undertake climate change impact assessments on buildings, \ncoastal engineering and construction, land use and other related areas, this study develops the web-based application Weather Morph: \nClimate Change Weather File Generator accessible to generate the future weather data for more than 2100 locations throughout the world for all \nfour IPCC (Intergovernmental Panel of Climate Change) emission scenarios in the three future time slices of the 2020s, 2050s and 2080s. \nThe output of the application is projected future weather datasets in formats TMY2 and EPW for general use.},\n  year = "2019"\n}\n\n
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\n The global climate change research has been conducted for a few years in various professional communities. In the building industry, researchers usually investigate the future building energy demands due to the climate change by simulation software. The input les to the simulation software includes projected weather data and building models. Although there exist a few mathematical methods to project the future weather, the morphing method is the most well-known among them. In the meantime, the simulation software and weather data are in a variety of formats depending on country of origin and/or simulation package. In order to provide both the research and the professional communities the possibility to undertake climate change impact assessments on buildings, coastal engineering and construction, land use and other related areas, this study develops the web-based application Weather Morph: Climate Change Weather File Generator accessible to generate the future weather data for more than 2100 locations throughout the world for all four IPCC (Intergovernmental Panel of Climate Change) emission scenarios in the three future time slices of the 2020s, 2050s and 2080s. The output of the application is projected future weather datasets in formats TMY2 and EPW for general use.\n
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\n \n\n \n \n \n \n \n \n Human-In-The-Loop Learning of Qualitative Preference Models.\n \n \n \n \n\n\n \n Allen, J.; Moussa, A.; and Liu, X.\n\n\n \n\n\n\n In Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference (FLAIRS), pages 108-111, 2019. AAAI Press\n \n\n\n\n
\n\n\n\n \n \n \"Human-In-The-LoopPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/flairs32/AMLiu,\n  author = {Joseph Allen and Ahmed Moussa and Xudong Liu},\n  booktitle = {Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference (FLAIRS)},\n  title = {Human-In-The-Loop Learning of Qualitative Preference Models},\n  pages     = {108-111},\n  publisher = {AAAI Press},\n\turl="https://aaai.org/ocs/index.php/FLAIRS/FLAIRS19/paper/view/18284",\n\tabstract = {In this work, we present a novel human-in-the-loop framework to help the human user understand the decision \nmaking process that involves choosing preferred options. We focus on qualitative preference models over alternatives from \ncombinatorial domains. This framework is interactive: the user provides her behavioral data to the framework, and the \nframework explains the learned model to the user. It is iterative: the framework collects feedback on the learned model from \nthe user and tries to improve it accordingly till the user terminates the iteration. In order to communicate the \nlearned preference model to the user, we develop visualization of intuitive and explainable graphic models, such as lexicographic \npreference trees and forests, and conditional preference networks. \nTo this end, we discuss key aspects of our framework for lexicographic preference models.\n},\n  year = 2019\n}\n\n
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\n In this work, we present a novel human-in-the-loop framework to help the human user understand the decision making process that involves choosing preferred options. We focus on qualitative preference models over alternatives from combinatorial domains. This framework is interactive: the user provides her behavioral data to the framework, and the framework explains the learned model to the user. It is iterative: the framework collects feedback on the learned model from the user and tries to improve it accordingly till the user terminates the iteration. In order to communicate the learned preference model to the user, we develop visualization of intuitive and explainable graphic models, such as lexicographic preference trees and forests, and conditional preference networks. To this end, we discuss key aspects of our framework for lexicographic preference models. \n
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\n \n\n \n \n \n \n \n \n Learning Optimal and Near-Optimal Lexicographic Preference Lists.\n \n \n \n \n\n\n \n Moussa, A.; and Liu, X.\n\n\n \n\n\n\n In Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference (FLAIRS), pages 128-131, 2019. AAAI Press\n \n\n\n\n
\n\n\n\n \n \n \"LearningPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/flairs32/MLiu,\n  author = {Ahmed Moussa and Xudong Liu},\n  booktitle = {Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference (FLAIRS)},\n  title = {Learning Optimal and Near-Optimal Lexicographic Preference Lists},\n  pages     = {128-131},\n  publisher = {AAAI Press},\n\turl="https://aaai.org/ocs/index.php/FLAIRS/FLAIRS19/paper/view/18289",\n\tabstract = {We consider learning problems of an intuitive and concise preference model, called lexicographic preference lists (LP-lists). \nGiven a set of examples that are pairwise ordinal preferences over a universe of objects built of attributes of discrete values, \nwe want to learn (1) an optimal LP-list that decides the maximum number of these examples, or (2) a near-optimal LP-list that decides as many examples as it \ncan. To this end, we introduce a dynamic programming based algorithm and a genetic algorithm for these two learning problems, respectively. \nFurthermore, we empirically demonstrate that the sub-optimal models computed by the genetic algorithm very well approximate the de facto \noptimal models computed by our dynamic programming based algorithm, and that the genetic algorithm outperforms the baseline greedy heuristic with \nhigher accuracy predicting new preferences.},\n  year = 2019\n}\n\n
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\n We consider learning problems of an intuitive and concise preference model, called lexicographic preference lists (LP-lists). Given a set of examples that are pairwise ordinal preferences over a universe of objects built of attributes of discrete values, we want to learn (1) an optimal LP-list that decides the maximum number of these examples, or (2) a near-optimal LP-list that decides as many examples as it can. To this end, we introduce a dynamic programming based algorithm and a genetic algorithm for these two learning problems, respectively. Furthermore, we empirically demonstrate that the sub-optimal models computed by the genetic algorithm very well approximate the de facto optimal models computed by our dynamic programming based algorithm, and that the genetic algorithm outperforms the baseline greedy heuristic with higher accuracy predicting new preferences.\n
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\n \n\n \n \n \n \n \n \n An Extensible and Personalizable Multi-Modal Trip Planner.\n \n \n \n \n\n\n \n Liu, X.; Fritz, C.; and Klenk, M.\n\n\n \n\n\n\n In Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference (FLAIRS), pages 124-127, 2019. AAAI Press\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 149 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/flairs32/LiuFK,\n  author = {Xudong Liu and Christian Fritz and Matthew Klenk},\n  booktitle = {Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference (FLAIRS)},\n  title = {An Extensible and Personalizable Multi-Modal Trip Planner},\n  pages     = {124-127},\n  publisher = {AAAI Press},\n\turl="https://aaai.org/ocs/index.php/FLAIRS/FLAIRS19/paper/view/18288",\n\tabstract = {Despite a tremendous amount of work in the literature and in the commercial sectors, current approaches to multi-modal \ntrip planning still fail to consistently generate plans that users deem optimal in practice. We believe that this is \ndue to the fact that current planners fail to capture the true preferences of users, e.g., their preferences depend on aspects that are not modeled. \nAn example of this could be a preference not to walk through an unsafe area at night. We present a novel multi-modal trip planner that allows users to \nupload auxiliary geographic data (e.g., crime rates) and to specify temporal constraints and preferences over these data in combination with typical metrics \nsuch as time and cost. Concretely, our planner supports the modes walking, biking, driving, public transit, and taxi, uses linear temporal logic to \ncapture temporal constraints, and preferential cost functions to represent preferences. We show by examples that this allows the expression \nof very interesting preferences and constraints that, naturally, lead to quite diverse optimal plans.},\n  year = 2019\n}\n\n
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\n Despite a tremendous amount of work in the literature and in the commercial sectors, current approaches to multi-modal trip planning still fail to consistently generate plans that users deem optimal in practice. We believe that this is due to the fact that current planners fail to capture the true preferences of users, e.g., their preferences depend on aspects that are not modeled. An example of this could be a preference not to walk through an unsafe area at night. We present a novel multi-modal trip planner that allows users to upload auxiliary geographic data (e.g., crime rates) and to specify temporal constraints and preferences over these data in combination with typical metrics such as time and cost. Concretely, our planner supports the modes walking, biking, driving, public transit, and taxi, uses linear temporal logic to capture temporal constraints, and preferential cost functions to represent preferences. We show by examples that this allows the expression of very interesting preferences and constraints that, naturally, lead to quite diverse optimal plans.\n
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\n  \n 2018\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Preference Learning and Optimization for Partial Lexicographic Preference Forests over Combinatorial Domains.\n \n \n \n \n\n\n \n Liu, X.; and Truszczynski, M.\n\n\n \n\n\n\n In Proceedings of the 10th International Symposium on Foundations of Information and Knowledge Systems (FoIKS), volume 10833, pages 284-302, 2018. Springer\n \n\n\n\n
\n\n\n\n \n \n \"PreferencePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{conf/foiks18/LiuT,\n  author = {Xudong Liu and Miroslaw Truszczynski},\n  booktitle = {Proceedings of the 10th International Symposium on Foundations of Information and Knowledge Systems (FoIKS)},\n  title = {Preference Learning and Optimization for Partial Lexicographic Preference Forests over Combinatorial Domains},\n\turl="https://link.springer.com/chapter/10.1007/978-3-319-90050-6_16",\n\tvolume = {10833},\n  pages = {284-302},\n  publisher = {Springer},\n  year = 2018\n}\n\n
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\n  \n 2015\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Learning Partial Lexicographic Preference Trees over Combinatorial Domains.\n \n \n \n \n\n\n \n Liu, X.; and Truszczynski, M.\n\n\n \n\n\n\n In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), pages 1539-1545, 2015. AAAI Press\n \n\n\n\n
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@inproceedings{conf/aaai15/LiuT,\n  author = {Xudong Liu and Miroslaw Truszczynski},\n  booktitle = {Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI)},\n  publisher = {AAAI Press},\n  title = {Learning Partial Lexicographic Preference Trees over Combinatorial Domains},\n  pages = {1539-1545},\n  isbn = {978-1-57735-698-1},\n\turl="https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9566",\n  abstract = {We introduce partial lexicographic preference trees (PLPtrees)\n\tas a formalism for compact representations of preferences\n\tover combinatorial domains. Our main results concern\n\tthe problem of passive learning of PLP-trees. Specifically, for\n\tseveral classes of PLP-trees, we study how to learn (i) a PLPtree\n\tconsistent with a dataset of examples, possibly subject to\n\trequirements on the size of the tree, and (ii) a PLP-tree correctly\n\tordering as many of the examples as possible in case\n\tthe dataset of examples is inconsistent. We establish complexity\n\tof these problems and, in all cases where the problem\n\tis in the class P, propose polynomial time algorithms.},\n  year = 2015\n}\n
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\n We introduce partial lexicographic preference trees (PLPtrees) as a formalism for compact representations of preferences over combinatorial domains. Our main results concern the problem of passive learning of PLP-trees. Specifically, for several classes of PLP-trees, we study how to learn (i) a PLPtree consistent with a dataset of examples, possibly subject to requirements on the size of the tree, and (ii) a PLP-tree correctly ordering as many of the examples as possible in case the dataset of examples is inconsistent. We establish complexity of these problems and, in all cases where the problem is in the class P, propose polynomial time algorithms.\n
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