Class Specific Feature Selection for Interval Valued Data Through Interval K-Means Clustering

TL;DR

This paper introduces a class-specific feature selection method for interval-valued data using a modified interval K-Means clustering, improving classification performance on standard datasets.

Contribution

It proposes a novel feature selection approach that adapts K-Means clustering for interval data to select class-specific features effectively.

Findings

Outperforms existing feature selection methods on standard datasets

Effective in selecting class-specific features for interval data

Improves classification accuracy with symbolic classifiers

Abstract

In this paper, a novel feature selection approach for supervised interval valued features is proposed. The proposed approach takes care of selecting the class specific features through interval K-Means clustering. The kernel of K-Means clustering algorithm is modified to adapt interval valued data. During training, a set of samples corresponding to a class is fed into the interval K-Means clustering algorithm, which clusters features into K distinct clusters. Hence, there are K number of features corresponding to each class. Subsequently, corresponding to each class, the cluster representatives are chosen. This procedure is repeated for all the samples of remaining classes. During testing the feature indices correspond to each class are used for validating the given dataset through classification using suitable symbolic classifiers. For experimentation, four standard supervised interval datasets are used. The results show the superiority of the proposed model when compared with the other existing state-of-the-art feature selection methods.