A feature selection method via analysis of relevance, redundancy, and interaction

Feature selection aims at selecting important features that can enhance learning performance in data mining, pattern recognition, and machine learning. Filter feature selection methods offer computational efficiency and feature evaluation criteria, while feature interaction information, which may greatly help increase classification accuracy, is often ignored. In this work, we instead propose a novel feature selection algorithm that uses the maximum of the maximum criterion to select highly relevant features and their maximally interactive features. Extensive experiments are performed to evaluate the performance of the proposed method with regard to the number of selected features and classification accuracy on thirty UCI datasets. The results demonstrate that the proposed algorithm not only efficiently selects the relevant features and the interactive features, but also enables classifiers to achieve classification accuracy that is better than, or comparably well to, ten representative competing feature selection algorithms.

Automated summary

The study introduces a novel feature selection algorithm that selects relevant and interactive features using a maximum criterion, leading to improved classification accuracy. Experimental results show that the algorithm efficiently selects features and enhances classifiers to achieve better or comparable classification accuracy compared to ten representative competing feature selection algorithms.