Abstract

Multiple birth support vector machine (MBSVM) is an extension of twin support vector machine on multi-class classification problem. In MBSVM, the size of each QP problem is restricted by the number of patterns in one of the K classes, so the computational complexity of MBSVM is much lower and the training speed of it is faster than the existing multi-class SVM. However, MBSVM neglects the structural information of data which may contain some significant prior knowledge for training classifiers. In this paper, we first present an improved version of structural least square twin support vector machine (S-LSTWSVM), called energy-based structural least square twin support vector machine (ES-LSTWSVM), which converts the constraints of the S-LSTWSVM into an energy-based model by introducing an energy for each hyperplane. Then we use the strategy of “rest-versus-one” in MBSVM to extend ES-LSTWSVM into the multi-class classification, called energy-based structural least squares MBSVM (ESLS-MBSVM). In order to prove the validity of ESLS-MBSVM, the experiment has been performed on UCI datasets. The experimental results show that our ESLS-MBSVM is effective and has good classification performance. In order to better illustrate the experimental results, we use Friedman test and ROC analysis for statistical comparisons.

中文翻译：

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基于能量的结构最小二乘MBSVM用于分类

摘要

多生支持向量机（MBSVM）是双生支持向量机在多类分类问题上的扩展。在MBSVM中，每个QP问题的大小受到K类之一中模式数量的限制，因此MBSVM的计算复杂度要低得多，并且其训练速度比现有的多类SVM快。但是，MBSVM忽略了可能包含一些重要的先验知识的数据的结构信息，这些知识对于训练分类器来说是不可缺少的。在本文中，我们首先提出结构最小二乘孪生支持向量机（S-LSTWSVM）的改进版本，称为基于能量的结构最小二乘孪生支持向量机（ES-LSTWSVM），它通过为每个超平面引入能量将S-LSTWSVM的约束转换为基于能量的模型。然后，我们在MBSVM中使用“相对于一”的策略将ES-LSTWSVM扩展到称为基于能量的结构最小二乘MBSVM（ESLS-MBSVM）的多类分类中。为了证明ESLS-MBSVM的有效性，对UCI数据集进行了实验。实验结果表明，我们的ESLS-MBSVM是有效的，具有良好的分类性能。为了更好地说明实验结果，我们使用Friedman检验和ROC分析进行统计比较。