Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification
Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification
复制标题
模糊粗糙集理论中基于信息增益比的属性选择及其在肿瘤分类中的应用
DOI：
10.1016/j.asoc.2012.07.029
复制
发表时间：
2013-01-01
影响因子：
8.7
通讯作者：
Xu, Qing
中科院分区：
文献类型：
--
作者：
Dai, Jianhua;Xu, Qing
基于基因表达水平的肿瘤分类对肿瘤诊断具有重要意义。由于肿瘤基因表达数据包含数千个属性，因此对肿瘤基因表达数据的属性选择成为肿瘤分类的关键。受决策树理论中增益比概念的启发，在模糊粗糙集理论框架下，提出了一种基于模糊增益比的属性选择方法。该方法与其他几种方法在三个真实世界的肿瘤基因表达数据集上进行了比较。结果表明，该方法是有效的。这项工作可能为处理基因表达或其他应用中的肿瘤数据提供一种可选策略。(C) 2012 Elsevier b.v.版权所有
Tumor classification based on gene expression levels is important for tumor diagnosis. Since tumor data in gene expression contain thousands of attributes, attribute selection for tumor data in gene expression becomes a key point for tumor classification. Inspired by the concept of gain ratio in decision tree theory, an attribute selection method based on fuzzy gain ratio under the framework of fuzzy rough set theory is proposed. The approach is compared to several other approaches on three real world tumor data sets in gene expression. Results show that the proposed method is effective. This work may supply an optional strategy for dealing with tumor data in gene expression or other applications. (C) 2012 Elsevier B. V. All rights reserved.