Class-specific feature selection using fuzzy information-theoretic metrics

Fuzzy information-theoretic metrics have been demonstrated to be effective in evaluating feature relevance and redundancy in both categorical and numerical feature selection tasks. Most existing feature selection methods based on fuzzy information-theoretic metrics treat all classes as a single entity, resulting in the selection of a consistent feature subset for all classes. This approach overlooks the fact that diverse classes may exhibit distinct discriminative characteristics, which requires the selection of diverse feature subsets for each class. Consequently, these methods lack the capability to handle this variability in feature selection required for optimal performance. To address this limitation, this paper proposes a class-specific feature selection method based on fuzzy information-theoretic metrics. To be more specific, we introduce several class-specific fuzzy information-theoretic metrics. Building upon these metrics, we formulate a class-specific feature selection algorithm, which is referred to as class-specific fuzzy information-theoretic feature selection. This algorithm enables the selection of highly relevant feature subsets tailored to each individual class. Furthermore, we present a class-specific ensemble classification framework that integrates the classification results obtained from the feature subsets generated by our method. Finally, we conduct extensive tests on 21 publicly available datasets using four popular classifiers to compare the performance of our method with eight up-to-date classification-specific methods. Test results demonstrate that our method outperforms the eight compared classification-specific methods in terms of performance.