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Open AccessArticle

Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data

1
School of Technology, Fuzhou University of International Studies and Trade, Fuzhou 350202, China
2
Department of Industrial Engineering and Management, St. John’s University, New Taipei City 25135, Taiwan
*
Authors to whom correspondence should be addressed.
Symmetry 2020, 12(2), 229; https://doi.org/10.3390/sym12020229
Received: 24 December 2019 / Revised: 18 January 2020 / Accepted: 29 January 2020 / Published: 3 February 2020
(This article belongs to the Special Issue Selected Papers from IIKII 2019 conferences in Symmetry)
In this paper, particle swarm optimization is incorporated into an improved bacterial foraging optimization algorithm, which is applied to classifying imbalanced data to solve the problem of how original bacterial foraging optimization easily falls into local optimization. In this study, the borderline synthetic minority oversampling technique (Borderline-SMOTE) and Tomek link are used to pre-process imbalanced data. Then, the proposed algorithm is used to classify the imbalanced data. In the proposed algorithm, firstly, the chemotaxis process is improved. The particle swarm optimization (PSO) algorithm is used to search first and then treat the result as bacteria, improving the global searching ability of bacterial foraging optimization (BFO). Secondly, the reproduction operation is improved and the selection standard of survival of the cost is improved. Finally, we improve elimination and dispersal operation, and the population evolution factor is introduced to prevent the population from stagnating and falling into a local optimum. In this paper, three data sets are used to test the performance of the proposed algorithm. The simulation results show that the classification accuracy of the proposed algorithm is better than the existing approaches. View Full-Text
Keywords: particle swarm optimization; improved bacterial foraging optimization; imbalanced data particle swarm optimization; improved bacterial foraging optimization; imbalanced data
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Ye, F.-L.; Lee, C.-Y.; Lee, Z.-J.; Huang, J.-Q.; Tu, J.-F. Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data. Symmetry 2020, 12, 229.

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