@inproceedings{5950912508ae456988cb0116ac0a26a3,
title = "IMF-PSO: A Particle Swarm Optimization Algorithm for Feature Selection in Classification",
abstract = "Feature selection is an important step in classification. Its goal is to find a set of features that can lead to high classification accuracy with a smaller number of features. This paper addresses feature selection as an optimization problem and solves it by a particle swarm optimization (PSO)-based approach. In the proposed PSO, we adopt three algorithmic components to enhance its performance: feature space adjustment, multi-swarm search, and local-best-guided improvement. We examine the effects of these components using seven data sets from the UCI repository. We also compare our algorithm with two existing algorithms. Experimental results show that the incorporated algorithmic components improve the algorithm performance and our algorithm outperforms the compared algorithms.",
keywords = "Classification, Feature Selection, Particle Swarm Optimization",
author = "Lu, {Cheng Ju} and Chiang, {Tsung Che}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 28th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2023 ; Conference date: 01-12-2023 Through 02-12-2023",
year = "2024",
doi = "10.1007/978-981-97-1711-8_8",
language = "English",
isbn = "9789819717101",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "110--125",
editor = "Chao-Yang Lee and Chun-Li Lin and Hsuan-Ting Chang",
booktitle = "Technologies and Applications of Artificial Intelligence - 28th International Conference, TAAI 2023, Proceedings",
}