Hierarchical Classification of Disaster News Using Local Classifier Per Parent Node. Aryal, A., Sharma, S., Thapa, S. K., Bhandari, S., Shakya, A., & Panday, S. P. In Raj, J. S., Perikos, I., & Balas, V. E., editors, Intelligent Sustainable Systems. Proceedings of International Conference on Intelligent Sustainable Systems [ICoISS 2023], volume 665, of Lecture Notes in Networks and Systems, pages 253–267, February, 2023. Springer, Singapore. ![Hierarchical Classification of Disaster News Using Local Classifier Per Parent Node [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 3 downloads Though a lot of work has been done to classify news headlines into generic categories like politics, sports, disasters, and so on, very little has been done to further classify a particular class of news into a deeper level of categories (especially in the case of disaster news). So, this paper presents a hierarchical classification of disaster news into appropriate disaster categories, based on their headlines. To demonstrate effectiveness of such classification, a hierarchical classifier, trained using local classifier per parent node approach and in mandatory leaf-node prediction setting, is compared with its flat counterpart on disaster news classification, and no significant difference of performance in accuracy is observed. However, the use of a hierarchical classifier as a substitute is, then, justified by demonstrating its advantage in a non-mandatory leafnode prediction setting, where a significant difference of performance in hP (hierarchical Precision) can be achieved by tuning the stopping criteria of the classifier.
@inproceedings{aryal2023hierarchical,
abstract = {Though a lot of work has been done to classify news headlines into generic categories like politics, sports, disasters, and so on, very little has been done to further classify a particular class of news into a deeper level of categories (especially in the case of disaster news). So, this paper presents a hierarchical classification of disaster news into appropriate disaster categories, based on their headlines. To demonstrate
effectiveness of such classification, a hierarchical classifier, trained using local classifier per parent node approach and in mandatory leaf-node prediction setting, is compared with its flat counterpart on disaster news classification, and no significant difference of performance in accuracy is observed. However, the use of a hierarchical classifier as a substitute is, then, justified by demonstrating its advantage in a non-mandatory leafnode prediction setting, where a significant difference of performance
in hP (hierarchical Precision) can be achieved by tuning the stopping criteria of the classifier.},
added-at = {2023-03-10T07:59:30.000+0100},
author = {Aryal, Aakalpa and Sharma, Sampanna and Thapa, Shrawan Kumar and Bhandari, Sudeep and Shakya, Aman and Panday, Sanjeeb Prasad},
biburl = {https://www.bibsonomy.org/bibtex/2193fb24b4bfec7ec17b8fe7cc3dec844/amanshakya},
booktitle = {Intelligent Sustainable Systems. Proceedings of International Conference on Intelligent Sustainable Systems [ICoISS 2023]},
doi = {https://doi.org/10.1007/978-981-99-1726-6_19},
editor = {Raj, Jennifer S. and Perikos, Isidoros and Balas, Valentina Emilia},
eventdate = {3-4, February 2023},
eventtitle = {International Conference on Intelligent Sustainable Systems [ICISS 2023]},
interhash = {ba0b992af1ed1bf115f71c11c71d3345},
intrahash = {193fb24b4bfec7ec17b8fe7cc3dec844},
isbn = {978-981-99-1726-6},
keywords = {myown ugc},
month = {February},
pages = {253–267},
publisher = {Springer, Singapore},
series = {Lecture Notes in Networks and Systems},
timestamp = {2023-06-17T18:10:29.000+0200},
title = {Hierarchical Classification of Disaster News Using Local Classifier Per Parent Node},
url = {https://link.springer.com/chapter/10.1007/978-981-99-1726-6_19},
venue = {Tirunelveli, India},
volume = 665,
year = 2023
}
Downloads: 3
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