A Twin Multi-Class Classification Support Vector Machine
A Twin Multi-Class Classification Support Vector Machine
复制标题
孪生多类分类支持向量机
DOI：
10.1007/s12559-012-9179-7
复制
发表时间：
2013-12-01
影响因子：
5.4
通讯作者：
Wang, Laisheng
中科院分区：
文献类型：
--
作者：
Xu, Yitian;Guo, Rui;Wang, Laisheng
孪生支持向量机（TSVM）是一种新的机器学习算法，它的目标是为每个类找到两个不平行的平面。为了做到这一点，需要解决一对较小的二次规划问题，而不是一个大的问题。经典的TSVM被提出用于二进制分类问题。然而，多类分类问题在我们的真实的世界中经常遇到。针对这一问题，本文提出了一种新的多类分类算法Twin-KSVC。它结合了TSVM和K-SVCR（支持向量分类-回归机叉类分类）的优点，并将所有训练点评估为“1-versus-1-versus-rest”结构，从而生成三元输出{ −1，0，+1}。由于所有的样本都被用于构建分类超平面，我们提出的算法产生了更高的分类精度相比，其他两种算法。在11个基准数据集上的实验结果证明了该算法的可行性和有效性。
Twin support vector machine (TSVM) is a novel machine learning algorithm, which aims at finding two nonparallel planes for each class. In order to do so, one needs to resolve a pair of smaller-sized quadratic programming problems rather than a single large one. Classical TSVM is proposed for the binary classification problem. However, multi-class classification problem is often met in our real world. For this problem, a new multi-class classification algorithm, called Twin-KSVC, is proposed in this paper. It takes the advantages of both TSVM and K-SVCR (support vector classification-regression machine fork-class classification) and evaluates all the training points into a “1-versus-1-versus-rest” structure, so it generates ternary outputs { −1, 0, +1}. As all the samples are utilized in constructing the classification hyper-plane, our proposed algorithm yields higher classification accuracy in comparison with other two algorithms. Experimental results on eleven benchmark datasets demonstrate the feasibility and validity of our proposed algorithm.