关键词: 离散二进制粒子群, 支持向量机（SVM）选择集成, 适应度函数, 调节因子

Abstract: For the low classification precision and excessive classifiers of SVM selection ensemble algorithm based on improved binary particle swarm optimization，this paper combines IBPSO and SVM selection ensemble algorithm to bring forward SVM selection ensemble algorithm based on IBPSO.As suitable fitness function and regulatory factor [k] are selected，for the SVM sets are generated by boostrap，both the precision and the number of classifiers of SVM selection ensemble algorithm based on IBPSO are superior to those of selection ensemble algorithm based on BPSO.The experiment proves the superiority of the former algorithm.

Key words: Binary Particle Swarm Optimization（BPSO）, Support Vector Machine（SVM） selection ensemble, fitness function, regulatory factor