NDLI: Active Learning through Adaptive Heterogeneous Ensembling

Content Provider	IEEE Xplore Digital Library
Author	Zhenyu Lu Xindong Wu Bongard, J.C.
Copyright Year	1989
Abstract	An open question in ensemble-based active learning is how to choose one classifier type, or appropriate combinations of multiple classifier types, to construct ensembles for a given task. While existing approaches typically choose one classifier type, this paper presents a method that trains and adapts multiple instances of multiple classifier types toward an appropriate ensemble during active learning. The method is termed adaptive heterogeneous ensembles (henceforth referred to as AHE). Experimental evaluations show that AHE constructs heterogeneous ensembles that outperform homogeneous ensembles composed of any one of the classifier types, as well as bagging, boosting and the random subspace method with random sampling. We also show in this paper that the advantage of AHE over other methods is increased if (1) the overall size of the ensemble also adapts during learning; and (2) the target data set is composed of more than two class labels. Through analysis we show that the AHE outperforms other methods because it automatically discovers complementary classifiers: for each data instance in the data set, instances of the classifier type best suited for that data point vote together, while instances of the other, inappropriate classifier types disagree, thereby producing a correct overall majority vote.
Sponsorship	IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society
Starting Page	368
Ending Page	381
Page Count	14
File Size	1835818
File Format	PDF
ISSN	10414347
Volume Number	27
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Bagging Boosting Entropy Electronic mail Uncertainty heterogeneous ensembles Ensemble methods active learning
Content Type	Text
Resource Type	Article
Subject	Information Systems Computational Theory and Mathematics Computer Science Applications

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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