A Boosting Algorithm for Label Covering in Multilabel Problems

Yonatan Amit, Ofer Dekel, Yoram Singer
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:27-34, 2007.

Abstract

We describe, analyze and experiment with a boosting algorithm for multilabel categorization problems. Our algorithm includes as special cases previously studied boosting algorithms such as Adaboost.MH. We cast the multilabel problem as multiple binary decision problems, based on a user-defined covering of the set of labels. We prove a lower bound on the progress made by our algorithm on each boosting iteration and demonstrate the merits of our algorithm in experiments with text categorization problems.

Cite this Paper

BibTeX
@InProceedings{pmlr-v2-amit07a, title = {A Boosting Algorithm for Label Covering in Multilabel Problems}, author = {Amit, Yonatan and Dekel, Ofer and Singer, Yoram}, booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics}, pages = {27--34}, year = {2007}, editor = {Meila, Marina and Shen, Xiaotong}, volume = {2}, series = {Proceedings of Machine Learning Research}, address = {San Juan, Puerto Rico}, month = {21--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v2/amit07a/amit07a.pdf}, url = {https://proceedings.mlr.press/v2/amit07a.html}, abstract = {We describe, analyze and experiment with a boosting algorithm for multilabel categorization problems. Our algorithm includes as special cases previously studied boosting algorithms such as Adaboost.MH. We cast the multilabel problem as multiple binary decision problems, based on a user-defined covering of the set of labels. We prove a lower bound on the progress made by our algorithm on each boosting iteration and demonstrate the merits of our algorithm in experiments with text categorization problems.} }
Endnote
%0 Conference Paper %T A Boosting Algorithm for Label Covering in Multilabel Problems %A Yonatan Amit %A Ofer Dekel %A Yoram Singer %B Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2007 %E Marina Meila %E Xiaotong Shen %F pmlr-v2-amit07a %I PMLR %P 27--34 %U https://proceedings.mlr.press/v2/amit07a.html %V 2 %X We describe, analyze and experiment with a boosting algorithm for multilabel categorization problems. Our algorithm includes as special cases previously studied boosting algorithms such as Adaboost.MH. We cast the multilabel problem as multiple binary decision problems, based on a user-defined covering of the set of labels. We prove a lower bound on the progress made by our algorithm on each boosting iteration and demonstrate the merits of our algorithm in experiments with text categorization problems.
RIS
TY - CPAPER TI - A Boosting Algorithm for Label Covering in Multilabel Problems AU - Yonatan Amit AU - Ofer Dekel AU - Yoram Singer BT - Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics DA - 2007/03/11 ED - Marina Meila ED - Xiaotong Shen ID - pmlr-v2-amit07a PB - PMLR DP - Proceedings of Machine Learning Research VL - 2 SP - 27 EP - 34 L1 - http://proceedings.mlr.press/v2/amit07a/amit07a.pdf UR - https://proceedings.mlr.press/v2/amit07a.html AB - We describe, analyze and experiment with a boosting algorithm for multilabel categorization problems. Our algorithm includes as special cases previously studied boosting algorithms such as Adaboost.MH. We cast the multilabel problem as multiple binary decision problems, based on a user-defined covering of the set of labels. We prove a lower bound on the progress made by our algorithm on each boosting iteration and demonstrate the merits of our algorithm in experiments with text categorization problems. ER -
APA
Amit, Y., Dekel, O. & Singer, Y.. (2007). A Boosting Algorithm for Label Covering in Multilabel Problems. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 2:27-34 Available from https://proceedings.mlr.press/v2/amit07a.html.

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