Title: A feature selection method via analysis of relevance, redundancy, and interaction
Abstract: 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.
Publication Year: 2021
Publication Date: 2021-11-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 45
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot