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Feature selection method based on artificial bee colony algorithm and support vector machines for medical datasets classification.

This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the...

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Published in:Scientific World Journal pp. 419187 - 419188
Main Authors: Uzer, Mustafa Serter, Yilmaz, Nihat, Inan, Onur
Format: research Journal Article
Published: Wiley-Blackwell 2013
Online Access:View this record in EBSCOhost
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      jtl: Scientific World Journal
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      dt: 2013
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        2012241048
        10.1155/2013/419187
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        104091046
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      tig:
        atl: Feature selection method based on artificial bee colony algorithm and support vector machines for medical datasets classification.
      aug:
        au:
          Uzer, Mustafa Serter
          Yilmaz, Nihat
          Inan, Onur
        affil: Electrical-Electronics Engineering, Faculty of Engineering, Selcuk University, Konya, Turkey.
      sug:
        subj:
          Algorithms
          Bees and Wasps
          Resource Databases
          Adult
          Aged
          Aged, 80 and Over
          Animals
          Disease Classification
          Female
          Middle Age
          Pregnancy
          Young Adult
          Adult: 19-44 years
          Aged: 65+ years
          Aged, 80 & over
          Middle Aged: 45-64 years
          Female
      ab: This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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