双重特征加权模糊支持向量机

针对当前基于特征加权的模糊支持向量机(FSVM)只考虑特征权重对隶属度函数的影响,而没有考虑在样本训练过程中将特征权重应用到核函数计算中的缺陷,提出了同时考虑特征加权对隶属度函数和核函数计算的影响的模糊支持向量机算法——双重特征加权模糊支持向量机(DFW-FSVM).首先,利用信息增益(IG)计算出每个特征的权重;然后...

Doubly feature-weighted fuzzy support vector machine

Concerning the shortcoming that the current feature-weighted Fuzzy Support Vector Machines （FSVM） only consider the influence of feature weights on the membership functions but ignore the application of feature weights to the kernel functions calculation during sample training， a new FSVM algorithm that considers the influence of feature weights on the membership function and the kernel function calculation simultaneously was proposed， namely Doubly Feature-Weighted FSVM （DFW-FSVM）. Firstly， relative weight of each feature was calculated by using Information Gain （IG）. Secondly， the weighted Euclidean distance between the sample and the class center was calculated in the original space based on the feature weights， and then the membership function was constructed by applying the weighted Euclidean distance； at the same time， the feature weights were applied to the calculation of the kernel function in the sample training process. Finally， DFW-FSVM algorithm was constructed according to the weighted membership functions and kernel functions. In this way， DFW-FSVM is able to avoid being dominated by trivial relevant or irrelevant features. The comparative experiments were carried out on eight UCI datasets， and the results show that compared with the best results of SVM， FSVM， Feature-Weighted SVM （FWSVM）， Feature-Weighted FSVM （FWFSVM） and FSVM based on Centered Kernel Alignment （CKA-FSVM） ， the accuracy and F1 value of the DFW-FSVM algorithm increase by 2.33 and 5.07 percentage points， respectively， indicating that the proposed DFW-FSVM has good classification performance.