基于一致性预测支持向量机的多分类算法

Multi-Classification Algorithm of Support Vector Machine via Conformal Predictors

针对一致性预测支持向量机的多分类问题,提出了两种多分类算法,分别是基于一致性预测一对多支持向量机算法(One-Vs-Rest Support Vector Machine Algorithm Via Conformal Predictors,OVR SVM CP)和基于一致性预测一对一支持向量机算法(One-Vs-One Support Vector Machine Algorithm Via Conformal Predictors,OVO SVM CP)。首先,将多分类问题转化为二分类问题,利用决策函数定义奇异值函数。然后,对这两种算法进行数值模拟实验,并与OVO SVM、OVO LSSVM、OVO TWSVM、HSVM算法相比较。最后,将两种算法应用于6组真实数据集测试其分类预测效果。仿真实验和真实数据应用结果表明,提出的两种算法预测效果较好,相比于其他3种的支持向量机算法有更高的预测准确率。

For the multi-classification problem of the support vector machine via conformal predictors,the authors propose two multi-classification algorithms in this paper,One-Vs-Rest Support Vector Machine Algorithm via Conformal Predictors(OVR SVM CP)and One-Vs-One Support Vector Machine Algorithm via Conformal Predictors(OVO SVM CP).Firstly,the multi-classification problem was transformed into a binary classification problem,and the nonconformity measure was defined by the decision function.Then,the numerical simulation experiments were conducted for the two algorithms,and then compared with the OVO SVM,OVO LSSVM and OVO TWSVM,HSVM algorithm.Finally,the two algorithms were applied to six real data sets to test their predictive effects.The results of simulation experiment and real data application show that the proposed two algorithms have good predictive effects and higher prediction accuracy than that of three other support vector machine algorithms.