Rough fuzzy classification for class imbalanced data

Abstract

This paper presents a new rough fuzzy classification approach for class imbalanced data. Here, interval type-2 fuzzy granulation of input features is formulated, various combinations of rough set extension-based methods are used to perform class imbalance learning, and K-nearest neighbor (KNN) classifier is used for data classification. The experimental results on the UCI data sets are reported to demonstrate the effectiveness of the proposed rough fuzzy classification model. Performance evaluation measures viz F-measure and geometric mean (G-mean) are used for analyzing classifier’s performance and suitability of the developed model for class imbalance learning.