特征加权支持向量机

该文针对现有的加权支持向量机(WSVM)和模糊支持向量机(FSVM)只考虑样本重要性而没有考虑特征重要性对分类结果的影响的缺陷，提出了基于特征加权的支持向量机方法，即特征加权支持向量机(FWSVM)。该方法首先利用信息增益计算各个特征对分类任务的重要度，然后用获得的特征重要度对核函数中的内积和欧氏距离进行加权计算，从而避免了核函数的计算被一些弱相关或不相关的特征所支配。理论分析和数值实验的结果都表明，该方法比传统的SVM具有更好的鲁棒性和分类能力。

Feature Weighted Support Vector Machine

Support vector machine has been applied in many research fields, such as pattern recognition and function estimate. There is a shortcoming in Weighted SVM and Fuzzy SVM, which take the importance of sample into account but neglect the relative importance of each feature with respect to the classification task. In this paper a SVM approach is proposed based on the feature weighting, i.e. Feature Weighted SVM (FWSVM). This method first estimates the relative importance (weight) of each feature by computing the information gain. Then, it utilizes the weights for computing the inner product and Euclidean distance in kernel functions. In this way the computing of kernel function can avoid being dominated by trivial relevant or irrelevant features. Theoretical analysis and experimental results show that the FWSVM is more robust and has the better performance of generalization than the traditional SVM.