论文标题
用于多类别分类的非平行超平面分类器
Nonparallel Hyperplane Classifiers for Multi-category Classification
论文作者
论文摘要
支持向量机（SVM）广泛用于解决分类和回归问题。最近，已经提出了各种非平行超平面分类算法（NHCA），与SVM相比，它们在分类准确性方面是可比的，但在计算上更有效。所有这些NHCA最初是针对二元分类问题提出的。由于大多数现实世界分类问题都涉及多个类，因此这些算法在多类方案中扩展。在本文中，我们介绍了四个NHCA的比较研究，即双SVM（TWSVM），广义特征值近端SVM（GEPSVM），正则化GEPSVM（REGGEPSVM）和改进的GEPSVM（IGEPSVM）（IGEPSVM）进行多类别分类。使用OneAgainst-All（OAA），基于二进制树（BT）和三元决策结构（TDS）方法实现了NHCA分类器的多类别分类算法，并且在基准Markmark UCI数据集上执行实验。实验结果表明，TDS-TWSVM在分类精度方面优于其他方法，而BT-RegGePSVM则花费最少的时间来构建分类器
Support vector machines (SVMs) are widely used for solving classification and regression problems. Recently, various nonparallel hyperplanes classification algorithms (NHCAs) have been proposed, which are comparable in terms of classification accuracy when compared with SVM but are computationally more efficient. All these NHCAs are originally proposed for binary classification problems. Since, most of the real world classification problems deal with multiple classes, these algorithms are extended in multi-category scenario. In this paper, we present a comparative study of four NHCAs i.e. Twin SVM (TWSVM), Generalized eigenvalue proximal SVM (GEPSVM), Regularized GEPSVM (RegGEPSVM) and Improved GEPSVM (IGEPSVM)for multi-category classification. The multi-category classification algorithms for NHCA classifiers are implemented using OneAgainst-All (OAA), binary tree-based (BT) and ternary decision structure (TDS) approaches and the experiments are performed on benchmark UCI datasets. The experimental results show that TDS-TWSVM outperforms other methods in terms of classification accuracy and BT-RegGEPSVM takes the minimum time for building the classifier
