AN IMPROVED FEATURE EXTRACTION APPROACH BASED ON ROUGH SETS FOR THE MEDICAL DIAGNOSIS

摘要：

This paper presents a novel approach based on Rough Sets to extract the complicated features from the medical diagnosis corpus. Some symptoms or basic features in the medical diagnosis are usually correlated. In general, the combinations of several basic symptoms may represent the disease more precision. However, the overmuch feature can reduce the generalization ability, or even many unfit features as the noise can decrease the models performance. This paper proposes to apply the rough set theory to mine the complicated features, even from noise or inconsistent corpus. Secondly, these complex features are added into the Maximum Entropy model or Support Vector Machine etc. as a new kind of features, consequently, the feature weights can be assigned according to the performance of the whole model. The experiments in the Liver-disorders repository show that our method can improve the Maximum Entropy model by the precision 3.51％, improve the Support Vector Machine model by the precision 3.05％, improve the NaTve Bayes model by the precision 3.59％, and improve the Bayes and Good luring model by the precision 3.59％.

关键词： Medical Diagnosis Rough Sets Mazimum Entropy Model Support Vector Machine Feature Eztraction

作者: WEI JIANG YI-JUN LI XIU-LI PANG

作者单位: Information Management Research Center, Harbin Institute of Technology, Harbin, 150001, P.R.China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 385-390

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）