Object recognition using enhanced particle swarm optimization.

Willis, Michael; Zhang, Li; Liu, Han; Xie, Hailun; Mistry, Kamlesh

Abstract

The identification of the most discriminative features in an explainable AI decision-making process is a challenging problem. This research tackles such challenges by proposing Particle Swarm Optimization (PSO) variants embedded with novel mutation and sampling iteration operations for feature selection in object recognition. Specifically, five PSO variants integrating different mutation and sampling strategies have been proposed to select the most discriminative feature subsets for the classification of different objects. A mutation strategy is firstly proposed by randomly flipping the particle positions in some dimensions to generate new feature interactions. Moreover, instead of embarking the position updating evolution in PSO, the proposed PSO variants generate offspring solutions through a sampling mechanism during the initial search process. Two offspring generation sampling schemes are investigated, i.e. the employment of the personal and global best solutions obtained using the mutation mechanism, respectively, as the starting positions for the subsequent search process. Subsequently, several machine learning algorithms are used in conjunction with the proposed PSO variants to perform object classification. As evidenced by the empirical results, the proposed PSO variants outperform the original PSO algorithm, significantly, for feature optimization.

Citation

WILLIS, M., ZHANG, L., LIU, H., XIE, H. and MISTRY, L. 2020. Object recognition using enhanced particle swarm optimization. In Proceedings of 2020 International conference machine learning and cybernetics (ICMLC 2020), 4 December 2020, [virtual event]. Piscataway: IEEE [online], pages 241-246. Available from: https://doi.org/10.1109/ICMLC51923.2020.9469584

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2020 International conference on machine learning and cybernetics (ICMLC 2020)
• Start Date: Dec 4, 2020
• Acceptance Date: Nov 15, 2020
• Online Publication Date: Dec 2, 2020
• Publication Date: Jul 5, 2021
• Deposit Date: Jul 27, 2021
• Publicly Available Date: Jul 27, 2021
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 241-246
• Series ISSN: 2160-1348
• ISBN: 9780738124261
• DOI: https://doi.org/10.1109/icmlc51923.2020.9469584
• Keywords: Feature selection; Object recognition; Optimization
• Public URL: https://rgu-repository.worktribe.com/output/1385946