Fuzzy multi-category proximal support vector classification via generalized eigenvalues
Fuzzy multi-category proximal support vector classification via generalized eigenvalues
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DOI：
10.1007/s00500-006-0130-2
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发表时间：
2007-02
期刊：
影响因子：
4.1
通讯作者：
Jayadeva;Reshma Khemchandani;S. Chandra
中科院分区：
文献类型：
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作者：
Jayadeva;Reshma Khemchandani;S. Chandra
给定一个数据集，其中每个点被标记为M个标签之一，我们提出了一种基于广义特征值的多类别近邻支持向量分类技术(MGEPSVMs)。与支持向量机不同，支持向量机通过将点分配到一个不相交的半空间来对点进行分类，在这里，点是通过将点分配到最接近其各自类别的非平行平面来进行分类的。当数据包含属于多个类别的样本时，类别通常重叠，并且针对多个非平行平面求解的分类器通常能够更好地解析测试样本。在多类别分类任务中，一个训练点可能与多个班级的原型具有相似性。该信息可以在模糊设置中使用。提出了一种利用训练样本隶属度信息的模糊多分类分类器，以提高分类器的泛化能力。通过对每个类别使用OFR(one-from-rest)分离，即1：M-1分类来获得所需的分类器。实验结果表明，该分类器比MGEP支持向量机具有更好的分类效果。
Given a dataset, where each point is labeled with one ofMlabels, we propose a technique for multi-category proximal support vector classification via generalized eigenvalues (MGEPSVMs). Unlike Support Vector Machines that classify points by assigning them to one ofMdisjoint half-spaces, here points are classified by assigning them to the closest ofMnon-parallel planes that are close to their respective classes. When the data contains samples belonging to several classes, classes often overlap, and classifiers that solve for several non-parallel planes may often be able to better resolve test samples. In multicategory classification tasks, a training point may have similarities with prototypes of more than one class. This information can be used in a fuzzy setting. We propose a fuzzy multi-category classifier that utilizes information about the membership of training samples, to improve the generalization ability of the classifier. The desired classifier is obtained by using one-from-rest (OFR) separation for each class, i.e. 1:M-1 classification. Experimental results demonstrate the efficacy of the proposed classifier over MGEPSVMs.