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A Multiple Resampling Method for Learning from Imbalanced Data Sets
Author(s) -
Estabrooks Andrew,
Jo Taeho,
Japkowicz Nathalie
Publication year - 2004
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.0824-7935.2004.t01-1-00228.x
Subject(s) - undersampling , oversampling , resampling , computer science , machine learning , artificial intelligence , class (philosophy) , scheme (mathematics) , task (project management) , data mining , pattern recognition (psychology) , mathematics , bandwidth (computing) , engineering , mathematical analysis , computer network , systems engineering
Resampling methods are commonly used for dealing with the class‐imbalance problem. Their advantage over other methods is that they are external and thus, easily transportable. Although such approaches can be very simple to implement, tuning them most effectively is not an easy task. In particular, it is unclear whether oversampling is more effective than undersampling and which oversampling or undersampling rate should be used. This paper presents an experimental study of these questions and concludes that combining different expressions of the resampling approach is an effective solution to the tuning problem. The proposed combination scheme is evaluated on imbalanced subsets of the Reuters‐21578 text collection and is shown to be quite effective for these problems.