Multi-class feature selection for texture classification
Multi-class feature selection for texture classification
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DOI：
10.1016/j.patrec.2006.03.013
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发表时间：
2006-10-15
影响因子：
5.1
通讯作者：
van Alphen, Deborah
中科院分区：
文献类型：
--
作者：
Chen, Xue-wen;Zeng, Xiangyan;van Alphen, Deborah
本文提出了一种基于递归特征消除（RFE）的多类特征选择方法，用于纹理分类。特征选择方案是在一对所有最小二乘支持向量机分类器（LS-SVM）的上下文中执行的。使用具有和不具有相关联特征的二进制分类器之间的裕度差来表征用于二进制分类的特征的鉴别能力。提出了一种新的最小-最大准则，用于混合二值分类器的排序列表进行多类特征选择。与传统的多类特征选择方法相比，该方法在特征数量较少的情况下具有更好的分类精度，特别是在小训练集的情况下。(c)2006 Elsevier B. V.保留所有权利。
In this paper, a multi-class feature selection scheme based on recursive feature elimination (RFE) is proposed for texture classifications. The feature selection scheme is performed in the context of one-against-all least squares support vector machine classifiers (LS-SVM). The margin difference between binary classifiers with and without an associated feature is used to characterize the discriminating power of features for the binary classification. A new criterion of min-max is used to mix the ranked lists of binary classifiers for multi-class feature selection. When compared to the traditional multi-class feature selection methods, the proposed method produces better classification accuracy with fewer features, especially in the case of small training sets. (c) 2006 Elsevier B.V. All rights reserved.