Affiliations: School of Mathematics and Statistics, Xidian University, Xi’an, Shaanxi, P.R. China
Correspondence:
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Corresponding author: Dandan Yan, School of Mathematics and Statistics, Xidian University, 266 Xinglong Section of Xifeng Road, Xi’an, Shaanxi 710126, P.R. China. Tel./Fax: +18509243087; E-mail: [email protected].
Abstract: Selecting model between recognition rate of “large” class and recognition rate of “small” class in imbalanced data is often a serious trade-off. Most approaches emphasize the accuracy of “large” class. The drawback is that potentially informative “small” class may be overlooked and even make an overfitting model. In this paper, we propose an alternative approach based on fuzzy system for classification problems with imbalanced data, called receive feedback model (RFM). It works by starting with a maximal attribution ratio probability that includes all observations for each class, and then gradually reclassify “unlabeled” samples if they succeed in minimal risk evaluation of a certain class. To exploit the RFM of classification problems, we further introduce probably approximately correct of the model and the convergence of our procedure. Extensive experiments using public data sets and the results of statistical tests have shown that the proposed RFM significantly outperforms other approaches in term of the appropriate trade-off both recognition rates of “large” class and “small” class.
