Michigan-Style Fuzzy Genetics-Based Machine Learning for Class Imbalance Data

Recently, interpretability of classifiers has been actively discussed in some real-world applications like medical and financial fields. Michigan-type Fuzzy Genetics-based Machine Learning (FGBML) is one of the most-well known methods for generating fuzzy classifiers with high interpretability. However, many real-world classification problems require high classification performance of a specific class which is less frequent than the others. For those problems, fuzzy classifiers generated by FGBML often have low classification performance for minority classes. Therefore, in this study, we extend FGBML for minority classes by applying the following four operations to the conventional Michigan-type FGBML: i) weighting the rule evaluation function by the number of patterns for each class, ii) changing the rule set evaluation function to the harmonic mean of the classification accuracy of each class, iii) selecting the base pattern in the heuristic rule generation based on the classification accuracy of each class, and iv) weighting the consequent part of each rule by the ratio of the number of the patterns between classes. Through computational experiment, we examine the effect of each change on the classification performance and show that the proposed method generates a fuzzy classifier with better classification performance than the conventional FGBML with SMOTE.