A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis
A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis
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DOI：
10.1016/j.eswa.2011.01.120
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发表时间：
2011-07-01
影响因子：
8.5
通讯作者：
Liu, Da-You
中科院分区：
文献类型：
--
作者：
Chen, Hui-Ling;Yang, Bo;Liu, Da-You
乳腺癌正在成为全世界妇女死亡的主要原因，同时，证实了这种疾病的早期发现和准确诊断可以确保患者的长期生存。专家系统和机器学习技术因其有效的分类和高诊断能力在该领域越来越受欢迎。本文提出了一种基于粗糙集（RS）的支持向量机分类器（RS_SVM）用于乳腺癌诊断。在本文提出的方法（RS_SVM）中，采用RS约简算法作为特征选择工具，去除冗余特征，进一步提高SVM的诊断准确率。在威斯康星乳腺癌数据集（WBCD）上，通过分类精度、灵敏度、特异性、混淆矩阵和受试者工作特征（ROC）曲线来检验RS_SVM的有效性。实验结果表明，所提出的RS_SVM不仅可以达到很高的分类精度，而且可以检测到5个信息特征的组合，为医生诊断乳房提供了重要的线索。(C) 2011 Elsevier Ltd.版权所有。
Breast cancer is becoming a leading cause of death among women in the whole world, meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. Expert systems and machine learning techniques are gaining popularity in this field because of the effective classification and high diagnostic capability. In this paper, a rough set (RS) based supporting vector machine classifier (RS_SVM) is proposed for breast cancer diagnosis. In the proposed method (RS_SVM), RS reduction algorithm is employed as a feature selection tool to remove the redundant features and further improve the diagnostic accuracy by SVM. The effectiveness of the RS_SVM is examined on Wisconsin Breast Cancer Dataset (WBCD) using classification accuracy, sensitivity, specificity, confusion matrix and receiver operating characteristic (ROC) curves. Experimental results demonstrate the proposed RS_SVM can not only achieve very high classification accuracy but also detect a combination of five informative features, which can give an important clue to the physicians for breast diagnosis. (C) 2011 Elsevier Ltd. All rights reserved.