Active Learning with Evolving Streaming Data. Žliobaitė, I., Bifet, A., Pfahringer, B., & Holmes, G. In Gunopulos, D., Hofmann, T., Malerba, D., & Vazirgiannis, M., editors, Machine Learning and Knowledge Discovery in Databases, pages 597–612, Berlin, Heidelberg, 2011. Springer. doi abstract bibtex In learning to classify streaming data, obtaining the true labels may require major effort and may incur excessive cost. Active learning focuses on learning an accurate model with as few labels as possible. Streaming data poses additional challenges for active learning, since the data distribution may change over time (concept drift) and classifiers need to adapt. Conventional active learning strategies concentrate on querying the most uncertain instances, which are typically concentrated around the decision boundary. If changes do not occur close to the boundary, they will be missed and classifiers will fail to adapt. In this paper we develop two active learning strategies for streaming data that explicitly handle concept drift. They are based on uncertainty, dynamic allocation of labeling efforts over time and randomization of the search space. We empirically demonstrate that these strategies react well to changes that can occur anywhere in the instance space and unexpectedly.
@inproceedings{zliobaite_active_2011,
address = {Berlin, Heidelberg},
title = {Active {Learning} with {Evolving} {Streaming} {Data}},
isbn = {978-3-642-23808-6},
doi = {10.1007/978-3-642-23808-6_39},
abstract = {In learning to classify streaming data, obtaining the true labels may require major effort and may incur excessive cost. Active learning focuses on learning an accurate model with as few labels as possible. Streaming data poses additional challenges for active learning, since the data distribution may change over time (concept drift) and classifiers need to adapt. Conventional active learning strategies concentrate on querying the most uncertain instances, which are typically concentrated around the decision boundary. If changes do not occur close to the boundary, they will be missed and classifiers will fail to adapt. In this paper we develop two active learning strategies for streaming data that explicitly handle concept drift. They are based on uncertainty, dynamic allocation of labeling efforts over time and randomization of the search space. We empirically demonstrate that these strategies react well to changes that can occur anywhere in the instance space and unexpectedly.},
language = {en},
booktitle = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}},
publisher = {Springer},
author = {Žliobaitė, Indrė and Bifet, Albert and Pfahringer, Bernhard and Holmes, Geoff},
editor = {Gunopulos, Dimitrios and Hofmann, Thomas and Malerba, Donato and Vazirgiannis, Michalis},
year = {2011},
pages = {597--612},
}
Downloads: 0
{"_id":"m69Y43i8heEk4vH4x","bibbaseid":"liobait-bifet-pfahringer-holmes-activelearningwithevolvingstreamingdata-2011","author_short":["Žliobaitė, I.","Bifet, A.","Pfahringer, B.","Holmes, G."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","address":"Berlin, Heidelberg","title":"Active Learning with Evolving Streaming Data","isbn":"978-3-642-23808-6","doi":"10.1007/978-3-642-23808-6_39","abstract":"In learning to classify streaming data, obtaining the true labels may require major effort and may incur excessive cost. Active learning focuses on learning an accurate model with as few labels as possible. Streaming data poses additional challenges for active learning, since the data distribution may change over time (concept drift) and classifiers need to adapt. Conventional active learning strategies concentrate on querying the most uncertain instances, which are typically concentrated around the decision boundary. If changes do not occur close to the boundary, they will be missed and classifiers will fail to adapt. In this paper we develop two active learning strategies for streaming data that explicitly handle concept drift. They are based on uncertainty, dynamic allocation of labeling efforts over time and randomization of the search space. We empirically demonstrate that these strategies react well to changes that can occur anywhere in the instance space and unexpectedly.","language":"en","booktitle":"Machine Learning and Knowledge Discovery in Databases","publisher":"Springer","author":[{"propositions":[],"lastnames":["Žliobaitė"],"firstnames":["Indrė"],"suffixes":[]},{"propositions":[],"lastnames":["Bifet"],"firstnames":["Albert"],"suffixes":[]},{"propositions":[],"lastnames":["Pfahringer"],"firstnames":["Bernhard"],"suffixes":[]},{"propositions":[],"lastnames":["Holmes"],"firstnames":["Geoff"],"suffixes":[]}],"editor":[{"propositions":[],"lastnames":["Gunopulos"],"firstnames":["Dimitrios"],"suffixes":[]},{"propositions":[],"lastnames":["Hofmann"],"firstnames":["Thomas"],"suffixes":[]},{"propositions":[],"lastnames":["Malerba"],"firstnames":["Donato"],"suffixes":[]},{"propositions":[],"lastnames":["Vazirgiannis"],"firstnames":["Michalis"],"suffixes":[]}],"year":"2011","pages":"597–612","bibtex":"@inproceedings{zliobaite_active_2011,\n\taddress = {Berlin, Heidelberg},\n\ttitle = {Active {Learning} with {Evolving} {Streaming} {Data}},\n\tisbn = {978-3-642-23808-6},\n\tdoi = {10.1007/978-3-642-23808-6_39},\n\tabstract = {In learning to classify streaming data, obtaining the true labels may require major effort and may incur excessive cost. Active learning focuses on learning an accurate model with as few labels as possible. Streaming data poses additional challenges for active learning, since the data distribution may change over time (concept drift) and classifiers need to adapt. Conventional active learning strategies concentrate on querying the most uncertain instances, which are typically concentrated around the decision boundary. If changes do not occur close to the boundary, they will be missed and classifiers will fail to adapt. In this paper we develop two active learning strategies for streaming data that explicitly handle concept drift. They are based on uncertainty, dynamic allocation of labeling efforts over time and randomization of the search space. We empirically demonstrate that these strategies react well to changes that can occur anywhere in the instance space and unexpectedly.},\n\tlanguage = {en},\n\tbooktitle = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}},\n\tpublisher = {Springer},\n\tauthor = {Žliobaitė, Indrė and Bifet, Albert and Pfahringer, Bernhard and Holmes, Geoff},\n\teditor = {Gunopulos, Dimitrios and Hofmann, Thomas and Malerba, Donato and Vazirgiannis, Michalis},\n\tyear = {2011},\n\tpages = {597--612},\n}\n\n","author_short":["Žliobaitė, I.","Bifet, A.","Pfahringer, B.","Holmes, G."],"editor_short":["Gunopulos, D.","Hofmann, T.","Malerba, D.","Vazirgiannis, M."],"key":"zliobaite_active_2011","id":"zliobaite_active_2011","bibbaseid":"liobait-bifet-pfahringer-holmes-activelearningwithevolvingstreamingdata-2011","role":"author","urls":{},"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":[],"search_terms":["active","learning","evolving","streaming","data","žliobaitė","bifet","pfahringer","holmes"],"title":"Active Learning with Evolving Streaming Data","year":2011}