Extract minimum positive and maximum negative features for imbalanced binary classification

Jinghua Wang, Jia You, Qin Li, Yong Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

24 Citations (Scopus)

Abstract

In an imbalanced dataset, the positive and negative classes can be quite different in both size and distribution. This degrades the performance of many feature extraction methods and classifiers. This paper proposes a method for extracting minimum positive and maximum negative features (in terms of absolute value) for imbalanced binary classification. This paper develops two models to yield the feature extractors. Model 1 first generates a set of candidate extractors that can minimize the positive features to be zero, and then chooses the ones among these candidates that can maximize the negative features. Model 2 first generates a set of candidate extractors that can maximize the negative features, and then chooses the ones that can minimize the positive features. Compared with the traditional feature extraction methods and classifiers, the proposed models are less likely affected by the imbalance of the dataset. Experimental results show that these models can perform well when the positive class and negative class are imbalanced in both size and distribution.
Original languageEnglish
Pages (from-to)1136-1145
Number of pages10
JournalPattern Recognition
Volume45
Issue number3
DOIs
Publication statusPublished - 1 Mar 2012

Keywords

  • Feature subspace extraction
  • Imbalanced binary classification
  • Maximum negative feature
  • Minimum positive feature
  • Pattern classification

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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