基于双重扰动的选择性支持向量机集成

为了进一步提升支持向量机泛化性能,提出一种基于双重扰动的选择性支持向量机集成算法。利用Boosting方法对训练集进行扰动基础上,采用基于相对核的粗糙集相对约简与重采样技术相结合的动态约简算法进行特征扰动以生成个体成员,然后基于负相关学习理论构造遗传个体适应度函数,利用加速遗传算法选择权重大于阈值的最优个体进行加权集成。实验结果表明,该算法具有较高的泛化性能和较低的时、空复杂性,是一种高效的集成方法。

Selective SVM ensemble based on double disturbance

This paper proposed a selective Support Vector Machine (SVM) ensemble algorithm based on double disturbance to improve the generalization ability of SVM. First, the training samples were disturbed by using conventional boosting algorithm, a dynamic reduction algorithm, which integrated relative reduction based on relative core of rough set and resample method, to produce individual SVM. The fitness function of genetic factors was established based on negative correlation learning, and Best SVM with weight larger than a given threshold value were selected by accelerating genetic algorithm and were integrated using weighted average. The experiments show that the algorithm has higher generalization performance, and lower time and space complexity. It is a highly effective ensemble algorithm.