CLASSIFICATION OF IMBALANCED DATA: A REVIEW

2009/06/01 by YANMIN SUN, ANDREW K. C. WONG, MOHAMED S. KAMEL · 1,407 citations

paper · doi:10.1142/s0218001409007326

published in International Journal of Pattern Recognition and Artificial Intelligence 23(04), 687-719 (World Scientific Pub Co Pte Lt)

crossref issued 2009/06/01 · crossref published 2009/06/01 · crossref published-print 2009/06/01 · crossref created 2009/06/17 · crossref published-online 2011/11/21 · crossref deposited 2019/08/07 · crossref indexed 2026/08/03

Abstract

Classification of data with imbalanced class distribution has encountered a significant drawback of the performance attainable by most standard classifier learning algorithms which assume a relatively balanced class distribution and equal misclassification costs. This paper provides a review of the classification of imbalanced data regarding: the application domains; the nature of the problem; the learning difficulties with standard classifier learning algorithms; the learning objectives and evaluation measures; the reported research solutions; and the class imbalance problem in the presence of multiple classes.

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