The Analysis of GCFS Algorithm in Medical Data Processing and Mining

Xiao Yu Chen; Bo Liu; Zhe Feng Zhang; Xin Xia

doi:doi:10.11648/j.ajsea.20140306.11

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The Analysis of GCFS Algorithm in Medical Data Processing and Mining

Xiao Yu Chen, Bo Liu, Zhe Feng Zhang, Xin Xia

Published in American Journal of Software Engineering and Applications (Volume 3, Issue 6)

Received: 20 November 2014 Accepted: 2 December 2014 Published: 5 December 2014

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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.

Published in	American Journal of Software Engineering and Applications (Volume 3, Issue 6)
DOI	10.11648/j.ajsea.20140306.11
Page(s)	68-73
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Feature Selection, GCFS, Ensemble learning

References

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APA Style

Xiao Yu Chen, Bo Liu, Zhe Feng Zhang, Xin Xia. (2014). The Analysis of GCFS Algorithm in Medical Data Processing and Mining. American Journal of Software Engineering and Applications, 3(6), 68-73. https://doi.org/10.11648/j.ajsea.20140306.11

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ACS Style

Xiao Yu Chen; Bo Liu; Zhe Feng Zhang; Xin Xia. The Analysis of GCFS Algorithm in Medical Data Processing and Mining. Am. J. Softw. Eng. Appl. 2014, 3(6), 68-73. doi: 10.11648/j.ajsea.20140306.11

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AMA Style

Xiao Yu Chen, Bo Liu, Zhe Feng Zhang, Xin Xia. The Analysis of GCFS Algorithm in Medical Data Processing and Mining. Am J Softw Eng Appl. 2014;3(6):68-73. doi: 10.11648/j.ajsea.20140306.11

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@article{10.11648/j.ajsea.20140306.11,
  author = {Xiao Yu Chen and Bo Liu and Zhe Feng Zhang and Xin Xia},
  title = {The Analysis of GCFS Algorithm in Medical Data Processing and Mining},
  journal = {American Journal of Software Engineering and Applications},
  volume = {3},
  number = {6},
  pages = {68-73},
  doi = {10.11648/j.ajsea.20140306.11},
  url = {https://doi.org/10.11648/j.ajsea.20140306.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20140306.11},
  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.},
 year = {2014}
}

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TY  - JOUR
T1  - The Analysis of GCFS Algorithm in Medical Data Processing and Mining
AU  - Xiao Yu Chen
AU  - Bo Liu
AU  - Zhe Feng Zhang
AU  - Xin Xia
Y1  - 2014/12/05
PY  - 2014
N1  - https://doi.org/10.11648/j.ajsea.20140306.11
DO  - 10.11648/j.ajsea.20140306.11
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 68
EP  - 73
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20140306.11
AB  - 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.
VL  - 3
IS  - 6
ER  -

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Author Information

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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APA Style

Xiao Yu Chen, Bo Liu, Zhe Feng Zhang, Xin Xia. (2014). The Analysis of GCFS Algorithm in Medical Data Processing and Mining. American Journal of Software Engineering and Applications, 3(6), 68-73. https://doi.org/10.11648/j.ajsea.20140306.11

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ACS Style

Xiao Yu Chen; Bo Liu; Zhe Feng Zhang; Xin Xia. The Analysis of GCFS Algorithm in Medical Data Processing and Mining. Am. J. Softw. Eng. Appl. 2014, 3(6), 68-73. doi: 10.11648/j.ajsea.20140306.11

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AMA Style

Xiao Yu Chen, Bo Liu, Zhe Feng Zhang, Xin Xia. The Analysis of GCFS Algorithm in Medical Data Processing and Mining. Am J Softw Eng Appl. 2014;3(6):68-73. doi: 10.11648/j.ajsea.20140306.11

Copy | Download

@article{10.11648/j.ajsea.20140306.11,
  author = {Xiao Yu Chen and Bo Liu and Zhe Feng Zhang and Xin Xia},
  title = {The Analysis of GCFS Algorithm in Medical Data Processing and Mining},
  journal = {American Journal of Software Engineering and Applications},
  volume = {3},
  number = {6},
  pages = {68-73},
  doi = {10.11648/j.ajsea.20140306.11},
  url = {https://doi.org/10.11648/j.ajsea.20140306.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20140306.11},
  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.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - The Analysis of GCFS Algorithm in Medical Data Processing and Mining
AU  - Xiao Yu Chen
AU  - Bo Liu
AU  - Zhe Feng Zhang
AU  - Xin Xia
Y1  - 2014/12/05
PY  - 2014
N1  - https://doi.org/10.11648/j.ajsea.20140306.11
DO  - 10.11648/j.ajsea.20140306.11
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 68
EP  - 73
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20140306.11
AB  - 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.
VL  - 3
IS  - 6
ER  -

Copy | Download