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Article

Active learning with committees for text categorization

Authors:

Prasad TadepalliAuthors Info & Claims

AAAI'97/IAAI'97: Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence

Pages 591 - 596

Published: 27 July 1997 Publication History

Abstract

In many real-world domains, supervised learning requires a large number of training examples. In this paper, we describe an active learning method that uses a committee of learners to reduce the number of training examples required for learning. Our approach is similar to the Query by Committee framework, where disagreement among the committee members on the predicted label for the input part of the example is used to signal the need for knowing the actual value of the label. Our experiments are conducted in the text categorization domain, which is characterized by a large number of features, many of which are irrelevant. We report here on experiments using a committee of Winnow-based learners and demonstrate that this approach can reduce the number of labeled training examples required over that used by a single Winnow learner by 1-2 orders of magnitude.

References

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Cited By

Aydogdu MSaraoglu HLouton D(2020)Using long short‐term memory neural networks to analyze SEC 13D filingsInternational Journal of Intelligent Systems in Accounting and Finance Management10.1002/isaf.146426:4(153-163)Online publication date: 14-Feb-2020
https://dl.acm.org/doi/10.1002/isaf.1464
(2017)Modeling of learning curves with applications to POS taggingComputer Speech and Language10.1016/j.csl.2016.06.00141:C(1-28)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.csl.2016.06.001
Zhang YCoutinho EZhang ZQuan CSchuller BZhang ZCohen PBohus DHoraud RMeng H(2015)Dynamic Active Learning Based on Agreement and Applied to Emotion Recognition in Spoken InteractionsProceedings of the 2015 ACM on International Conference on Multimodal Interaction10.1145/2818346.2820774(275-278)Online publication date: 9-Nov-2015
https://dl.acm.org/doi/10.1145/2818346.2820774
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Active learning with committees for text categorization
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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cover image Guide Proceedings

AAAI'97/IAAI'97: Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence

July 1997

1085 pages

ISBN:0262510952

Publisher

AAAI Press

Publication History

Published: 27 July 1997

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Cited By

Aydogdu MSaraoglu HLouton D(2020)Using long short‐term memory neural networks to analyze SEC 13D filingsInternational Journal of Intelligent Systems in Accounting and Finance Management10.1002/isaf.146426:4(153-163)Online publication date: 14-Feb-2020
https://dl.acm.org/doi/10.1002/isaf.1464
(2017)Modeling of learning curves with applications to POS taggingComputer Speech and Language10.1016/j.csl.2016.06.00141:C(1-28)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.csl.2016.06.001
Zhang YCoutinho EZhang ZQuan CSchuller BZhang ZCohen PBohus DHoraud RMeng H(2015)Dynamic Active Learning Based on Agreement and Applied to Emotion Recognition in Spoken InteractionsProceedings of the 2015 ACM on International Conference on Multimodal Interaction10.1145/2818346.2820774(275-278)Online publication date: 9-Nov-2015
https://dl.acm.org/doi/10.1145/2818346.2820774
Zhang ZCoutinho EDeng JSchuller B(2015)Cooperative learning and its application to emotion recognition from speechIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2014.237555823:1(115-126)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1109/TASLP.2014.2375558
Mahmoudi MTaghiyareh FAraabi B(2013)A neuro-fuzzy immune inspired classifier for task-oriented textsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.5555/2596266.259628325:3(673-683)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.5555/2596266.2596283
Chakraborty SBalasubramanian VPanchanathan S(2013)Generalized batch mode active learning for face-based biometric recognitionPattern Recognition10.1016/j.patcog.2012.07.02546:2(497-508)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1016/j.patcog.2012.07.025
Ngonga Ngomo ALyko K(2012)EAGLEProceedings of the 9th international conference on The Semantic Web: research and applications10.1007/978-3-642-30284-8_17(149-163)Online publication date: 27-May-2012
https://dl.acm.org/doi/10.1007/978-3-642-30284-8_17
Benbrahim H(2011)Fuzzy semi-supervised support vector machinesProceedings of the 7th international conference on Machine learning and data mining in pattern recognition10.5555/2033831.2033842(127-139)Online publication date: 30-Aug-2011
https://dl.acm.org/doi/10.5555/2033831.2033842
Mesterharm CPazzani MApte CGhosh JSmyth P(2011)Active learning using on-line algorithmsProceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2020408.2020553(850-858)Online publication date: 21-Aug-2011
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Zhang XZhao DChen LMin W(2009)Batch mode active learning based multi-view text classificationProceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 710.5555/1802134.1802239(472-476)Online publication date: 14-Aug-2009
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