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Granular Computing Combined with Support Vector Machines for Diagnosing Erythemato-Squamous Diseases

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Book cover Health Information Science (HIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10594))

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Abstract

A computational model with a new hybrid feature selection approach is developed in this paper to determine the type of erythemato-squamous disease. The new feature selection method combines the strength of granular computing (GrC) and support vector machines (SVM) together with the advantages of filters and wrappers to select the optimal feature subset to build a sound classifier. We treat the erythemato-squamous disease dataset as a decision information system, where the sample features are considered as condition attributes and the class label the decision attribute. We calculate the granular of each feature and decision attribute, then evaluate the significance of each feature to classification by the difference between its granularity and that of decision attribute, after that we rank features in descending order by their significance. Generalized sequential forward search (GSFS) strategy together with SVM is adopted to select the necessary features to condense decision information system without compromising its classification capacity. 5-fold cross validation experiments have been conducted on the erythemato-squamous disease dataset taken from UCI (University of California Irvine) machine learning repository. Experimental results demonstrate that our diagnostic model has got condensed decision information system for erythemato-squamous disease with less features than the original ones while achieving a comparable accuracy in the literature.

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Acknowledgement

We are most grateful to H. Altay Guvenir who created the erythemato-squamous dataset as well as to G.C. Cawley who provides the helpful SVM tool box. This work is supported in part by the National Natural Science Foundation of China under Grant No. 61673251, is also supported by the Key Science and Technology Program of Shaanxi Province of China under Grant No. 2013K12-03-24, and is at the same time supported by the Fundamental Research Funds for the Central Universities under Grant No. GK201701006.

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Authors and Affiliations

  1. Information Science and Technology, Aichi Prefrctural University, Nagakute, Aichi, 480-1198, Japan

    Yongchao Wang

  2. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, People’s Republic of China

    Yongchao Wang

  3. School of Computer Science, Shaanxi Normal University, Xi’an, 710062, People’s Republic of China

    Juanying Xie

Authors
  1. Yongchao Wang
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  2. Juanying Xie
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Corresponding author

Correspondence to Juanying Xie .

Editor information

Editors and Affiliations

  1. Centre for Applied Informatics, Victoria University, Melbourne, Victoria, Australia

    Siuly Siuly

  2. Vrije University of Amsterdam, Amsterdam, The Netherlands

    Zhisheng Huang

  3. University of Nottingham, Nottingham, United Kingdom

    Uwe Aickelin

  4. Swinburne University of Technology, Melbourne, Victoria, Australia

    Rui Zhou

  5. Victoria University, Melbourne, Victoria, Australia

    Hua Wang

  6. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

  7. Institute for High Energy Physics, Protvino, Russia

    Stanislav Klimenko

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Wang, Y., Xie, J. (2017). Granular Computing Combined with Support Vector Machines for Diagnosing Erythemato-Squamous Diseases. In: Siuly, S., et al. Health Information Science. HIS 2017. Lecture Notes in Computer Science(), vol 10594. Springer, Cham. https://doi.org/10.1007/978-3-319-69182-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-69182-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69181-7

  • Online ISBN: 978-3-319-69182-4

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