关键词: 不平衡数据, 改进的细菌觅食优化, 粒子群优化

Abstract:

To overcome the shortcoming of Bacterial Foraging Optimization（BFO） algorithm easily falling into a local optimum, this paper proposes hybrid Particle Swarm Optimization（PSO） and Improved Bacterial Foraging Optimization（IBFO） algorithm to classify imbalanced data. Three datasets are used for testing the performance of the proposed algorithm. One is the real ovarian cancer microarray data, and the other two from the UCI repository are spam email dataset and zoo dataset. The Borderline Synthetic Minority Oversampling Technique（Borderline-SMOTE） and Tomek Link are used to pre-process imbalanced data. Thereafter, the proposed algorithm is used to classify imbalanced data. In the improvement of the bacterial foraging optimization algorithm, firstly, the chemotaxis process is improved. The particle swarm optimization algorithm is used to search first and then treats the result as bacteria, which improves the global searching ability of the bacterial foraging optimization. Secondly, the reproduction operation process is improved, and the selection standard of the survival of the fittest is improved. Finally, the elimination and dispersal operation process is improved, so as to prevent the population from falling into local optimum and preventing evolution stagnation. The simulated results reveal that the classification accuracy of the proposed algorithm is better than the existing approaches.

Key words: imbalanced data, improved bacterial foraging optimization, particle swarm optimization