基于遗传算法的维权重支持向量机研究

A study of attribute weighted SVM based on Genetic Algorithm

与采用信息增益值来为样本属性加权的方法相比,本文提出了一种维权重支持向量机方法,该方法采用遗传算法为样本属性加权同时优化支持向量机及其核函数的参数,形成基于遗传算法的维权重支持向量机方法.在UCI数据集上的多个对比性实验表明本文方法可以进一步提高分类器的学习和泛化性能.

Compared with the approach that the weight of each attribute is calculated by applying the information gain,this paper proposes an attribute weighted Support Vector Machine(SVM) approach which gives weight to each attribute and simultaneous optimizes parameters of SVM and kernel function based on Genetic Algorithm(GA).The experimental results based on UCI machine learning repository show that such an approach can improve the learning and generalization abilities of the classifier.