基于遗传算法的支持向量机分类算法

针对现有部分支持向量机在多类分类过程中存在的数据不均衡性、对算法结构依赖性强的问题,提出一种新的基于遗传算法的支持向量机多类分类算法。以遗传算法中的交叉作为支持向量机中类的选择,以变异改善分类过程中的纠错能力,以适应度函数作为最优分类结果的确定。在不同特性的样本集上进行仿真测试,结果证明,该算法在类数较多的情况下,有更好的数据均衡性,在分类速度及准确度上均有一定的优越性。

Support Vector Machine Classification Algorithm Based on Genetic Algorithms

In view of the some existing SVMS issues such as data disproportion and strong dependence on the algorithm structure in the process of multi-classification,a new SVM classification algorithm based on genetic algorithms is proposed.The new algorithm considers the crossover in the genetic algorithm as the choice of divisions in the SVM,improves the correction capability during classification by mutation and bases the optimal classification results on the fitness function.Simulation and tests are performed on sample sets with different characteristics and the result shows the algorithm has superiority in data balance,speed and accuracy of classification in the case of multi-classification.