Generating fuzzy rule base classifier for highly imbalanced datasets using a hybrid of evolutionary algorithms and subtractive clustering

In this paper, a design methodology is proposed for generating a fuzzy rule-based classifier for highly imbalanced datasets (only binary classification problems). The classifier is based on sugeno-type fuzzy inference system (FIS) and is generated using subtractive clustering, differential evolution (DE) and multi-gene genetic programming (MGGP) to obtain fuzzy rules. Subtractive clustering and DE are utilized for producing antecedents of rules and MGGP is employed for generating the functions in the consequence parts of rules. Feature selection is utilized as an important pre-processing step for dimension reduction. Performance of the proposed method is compared with some fuzzy rule-based classification approaches taken from the literature. The experiments are performed over 22 highly imbalanced datasets from KEEL dataset repository; the classification results are evaluated using AUC as a performance measure. Some statistical non-parametric tests are used to compare classification performance of different methods in different datasets. The obtained results reveal that the proposed classifier outperforms other methods in terms of AUC values.