NDLI: Learning classifiers from imbalanced data based on biased minimax probability machine

Content Provider	IEEE Xplore Digital Library
Author	Kaizhu Huang Haiqin Yang King, I. Lyu, M.R.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, China (Kaizhu Huang; Haiqin Yang; King, I.; Lyu, M.R.)
Abstract	We consider the problem of the binary classification on imbalanced data, in which nearly all the instances are labelled as one class, while far fewer instances are labelled as the other class, usually the more important class. Traditional machine learning methods seeking an accurate performance over a full range of instances are not suitable to deal with this problem, since they tend to classify all the data into the majority, usually the less important class. Moreover, some current methods have tried to utilize some intermediate factors, e.g., the distribution of the training set, the decision thresholds or the cost matrices, to influence the bias of the classification. However, it remains uncertain whether these methods can improve the performance in a systematic way. In this paper, we propose a novel model named biased minimax probability machine. Different from previous methods, this model directly controls the worst-case real accuracy of classification of the future data to build up biased classifier;. Hence, it provides a rigorous treatment on imbalanced data. The experimental results on the novel model comparing with those of three competitive methods, i.e., the naive Bayesian classifier, the k-nearest neighbor method, and the decision tree method C4.5, demonstrate the superiority of our novel model.
Sponsorship	IEEE Comput. Soc
File Size	377358
File Format	PDF
ISBN	0769521584
ISSN	10636919
DOI	10.1109/CVPR.2004.1315213
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning Minimax techniques Costs Sampling methods Classification tree analysis Educational institutions Computer science Data engineering Learning systems Bayesian methods
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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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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