International Journal of Computational Intelligence Systems

Volume 11, Issue 1, January 2018, Pages 1 - 14

Bare bones particle swarm optimization with adaptive chaotic jump for feature selection in classification

Authors
Chenye Qiuqiuchenye@njupt.edu.cn
School of Internet of Things, Nanjing University of Posts and Telecommunications, No.66 Xinmofan Road, Nanjing, Jiangsu, 210003, China
Received 11 April 2017, Accepted 13 September 2017, Available Online 1 January 2018.
DOI
https://doi.org/10.2991/ijcis.11.1.1How to use a DOI?
Keywords
feature selection, bare bones particle swarm, adaptive chaotic jump, global best updating mechanism
Abstract

Feature selection (FS) is a crucial data pre-processing process in classification problems. It aims to reduce the dimensionality of the problem by eliminating irrelevant or redundant features while achieve similar or even higher classification accuracy than using all the features. As a variant of particle swarm optimization (PSO), Bare bones particle swarm optimization (BBPSO) is a simple but very powerful optimizer. However, it also suffers from premature convergence like other PSO algorithms, especially in high-dimensional optimization problems. In order to improve its performance in FS problems, this paper proposes a novel BBPSO based FS method called BBPSO-ACJ. An adaptive chaotic jump strategy is designed to help the stagnated particles make a large change in their searching trajectory. It can enrich the search behavior of BBPSO and prevent the particles from being trapped into local attractors. A new global best updating mechanism is employed to reduce the size of obtained feature subset. The proposed BBPSO-ACJ is compared with eight evolutionary computation (EC) based wrapper methods and two filter methods on nine benchmark datasets with different number of dimensions and instances. The experimenta l results indicate that the proposed method can select the most discriminative features from the entire feature set and achieve significantly better classification performance than other comparative methods.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
11 - 1
Pages
1 - 14
Publication Date
2018/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.11.1.1How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Chenye Qiu
PY  - 2018
DA  - 2018/01
TI  - Bare bones particle swarm optimization with adaptive chaotic jump for feature selection in classification
JO  - International Journal of Computational Intelligence Systems
SP  - 1
EP  - 14
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.1
DO  - https://doi.org/10.2991/ijcis.11.1.1
ID  - Qiu2018
ER  -