SIA OpenIR  > 数字工厂研究室
Feature Selection Based on Multiple Correlation Measures for Medical Examination Dataset
Li KT(李开拓); Peng H(彭慧); Zhou XF(周晓锋); Li S(李帅)
Department数字工厂研究室
Conference Name2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IEEE IMCEC 2016
Conference DateJune 12-15, 2016
Conference PlaceGuilin, China
Source Publication2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IEEE IMCEC 2016
PublisherIEEE
Publication PlaceNew York
2016
Pages845-849
Indexed ByEI ; CPCI(ISTP)
EI Accession number20171403534938
WOS IDWOS:000405512400169
Contribution Rank1
ISBN978-1-4673-9613-4
KeywordFilter Feature Selection Filter Feature Interaction Medical Examination
AbstractThe analysis of medical data is not an easy task for health care systems since a comprehensive medical examination is performed with hundreds of parameters. These data come with considerable amount of irrelevant and redundant features. But only a subset of these features are useful for prediction, feature selection plays a significant part in to identify critical features for building prediction models. However, a number of existing filter feature selection methods can cope with redundancy and irrelevance by using single correlation measure, and ignore the interaction between features as well. In this paper, the idea of using multiple correlation measures is adopted for filter feature selection, additionally, the used method is also take feature interaction into account. Two correlation measures that converted to the standard form of 0-1 are fused together in the used algorithm, and the complementary correlation between candidate feature and selected feature can be distinguished by introducing an item. According to four common classifiers and a medical examination dataset, the experiments can be developed in order to demonstrate the validity of the method. Experimental results can confirm that the method is superior to four representative filter feature selection methods.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/19486
Collection数字工厂研究室
Corresponding AuthorLi KT(李开拓)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.University of Chinese Academy of Sciences, Beijing, 100049, China
3.Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, 110016, China
Recommended Citation
GB/T 7714
Li KT,Peng H,Zhou XF,et al. Feature Selection Based on Multiple Correlation Measures for Medical Examination Dataset[C]. New York:IEEE,2016:845-849.
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