Recently, several studies have claimed that using class-specific feature
subsets provides certain advantages over using a single feature subset for
representing the data for a classification problem. Unlike traditional feature
selection methods, the class-specific feature selection methods select an
optimal feature subset for each class. Typically class-specific feature
selection (CSFS) methods use one-versus-all split of the data set that leads to
issues such as class imbalance, decision aggregation, and high computational
overhead. We propose a class-specific feature selection method embedded in a
fuzzy rule-based classifier, which is free from the drawbacks associated with
most existing class-specific methods. Additionally, our method can be adapted
to control the level of redundancy in the class-specific feature subsets by
adding a suitable regularizer to the learning objective. Our method results in
class-specific rules involving class-specific subsets. We also propose an
extension where different rules of a particular class are defined by different
feature subsets to model different substructures within the class. The
effectiveness of the proposed method has been validated through experiments on
three synthetic data sets