采用类电磁机制算法的SVM决策树多分类策略

基于分类问题的特点，设计了适用于分类问题的类电磁机制算法，然后设计了基于改造后的类电磁机制算法的最优决策树生成算法，用以解决支持向量机多分类问题．以最大分类间隔为准则，利用类电磁机制算法进行优化，从而生成最优或次优的决策树．在每个决策结点利用传统的支持向量机二分类方法进行分类，最终实现支持向量机多分类．仿真结果表明：这种方法比传统的1-a-1，1-a-r，DAG-SVM，DT-SVM以及GADT-SVM方法有更优的性能．

SVM decision-tree multi-classification strategy via electromagnetism-like mechanism

Based on the characteristics of the classification problem, a modified electromagnetism-like mechanism (EM) algorithm is designed, which is suitable for classification. Then a modified EM-based optimal decision-tree algorithm is proposed to deal with the SVM multi-class classification problem. First, EM is used to create an optimal or near-optimal decision tree automatically, which makes the margin between two classes maximal at every decision node. Then at every decision node, standard SVM is used to make binary classification. Finally, the SVM decision tree achieves multi-classification. Experimental results show that the proposed method is better than the traditional methods such as “1-a-1”, “1-a-r”, “DAG-SVM”,“DT-SVM” and “GADT-SVM”.