Fuzzy support vector machines for multilabel classification
Fuzzy support vector machines for multilabel classification
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DOI：
10.1016/j.patcog.2015.01.009
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发表时间：
2015-06-01
影响因子：
8
通讯作者：
Abe, Shigeo
中科院分区：
文献类型：
--
作者：
Abe, Shigeo
一对一支持向量机用于多标签分类的问题是，数据样本可能被分类到一个未定义的多标签类别中，也可能不被分类到任何类别中。为了解决这一问题，本文提出了模糊支持向量机的多标签分类方法，对于每个多标签分类，定义一个具有相关隶属函数的区域，并将数据点分类为隶属函数最大的多标签分类。计算机实验表明，与传统的一对多支持向量机相比，模糊支持向量机的分类精度有所提高。(C)2015爱思唯尔有限公司。保留所有权利。
The problem of one-against-all support vector machines (SVMs) for multilabel classification is that a data sample may be classified into a multilabel class that is not defined or it may not be classified into any class. To solve this problem, in this paper we propose fuzzy SVMs (FSVMs) for multilabel classification, in which for each multilabel class, a region with the associated membership function is defined and a data point is classified into a multilabel class whose membership function is the largest. By computer experiments, we show that the accuracy is improved by the FSVM over the conventional one-against-all SVM. (C) 2015 Elsevier Ltd. All rights reserved.