基于PSO拓展的多分类器加权集成方法

Multiple Classifiers Weighted Integration Method Based on Particle Swarm Optimization Expanded

在多分类器集成时,每个基分类器的效能不同,如每个权值都相同,则会影响基分类器发挥作用。基于此,提出基于PSO拓展的多分类器加权集成方法 BCPSO。该方法采用随机子空间生成各个独立的子分类器,输出结果通过各分类器加权投票组合规则集成。实验结果表明,该方法有效可行,具有较高的分类正确率。

For the integration of multiple classifiers, each base classifier performance is different, if the weights are the same for each classifier, it is bound to affect the classification of base classifier. This paper proposes a weighting method Binary Crossover Particle Swarm Optimization （BCPSO）, in which each individual sub-classifier uses random subspace method to generate and output the final classification by the combination of rule by weighted voting. Experimental results show that the method is feasible and effective, it has high classification accuracy.