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The Analysis of GCFS Algorithm in Medical Data Processing and Mining
American Journal of Software Engineering and Applications
Volume 3, Issue 6, December 2014, Pages: 68-73
Received: Nov. 20, 2014; Accepted: Dec. 2, 2014; Published: Dec. 5, 2014
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Authors
Xiao Yu Chen, Department of Information Centre, East Hospital, Tongji University, School of Medicine, Shanghai, China
Bo Liu, Department of Information Centre, East Hospital, Tongji University, School of Medicine, Shanghai, China
Zhe Feng Zhang, Department of Information Centre, East Hospital, Tongji University, School of Medicine, Shanghai, China
Xin Xia, Department of Information Centre, East Hospital, Tongji University, School of Medicine, Shanghai, China
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Abstract
Feature selection plays a significant part in medical data processing and mining, it can reduce the dimensionalities of datasets and enhance the performance of the classifiers, and it is also helpful to clinical decision support to a great extent. At present, the clinical decision support is more performed by physicians subjectively based on clinical knowledge, which may hinder the diagnosis and treatment. This paper mainly outlines the performance of GCFS (Genetic Correlation-based Feature Selection) algorithm in the processing and mining procedure of medical data, and medical UCI datasets are employed as the studied materials for proving the improvement of feature selection in data classification. Compared with the algorithms of CFS and GA (Genetic Algorithm), ensemble learning methods are employed as the testing classifiers, and the results show GCFS algorithm almost improves the performances of the testing classifiers better than CFS and GA.
Keywords
Feature Selection, GCFS, Ensemble learning
To cite this article
Xiao Yu Chen, Bo Liu, Zhe Feng Zhang, Xin Xia, The Analysis of GCFS Algorithm in Medical Data Processing and Mining, American Journal of Software Engineering and Applications. Vol. 3, No. 6, 2014, pp. 68-73. doi: 10.11648/j.ajsea.20140306.11
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