Combination of Multiple Classifiers for Classifying the Diabetic Data(<Special Issue>Biosensors: Data Acquisition, Processing and Control)

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概要

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• Multiple classifier combination is a technique that combines the decisions of different classifiers. Combination can reduce the execution time of classification, variance of estimation errors, thereby improving the overall classification accuracy. This paper introduces a genetic algorithm able to combine three different classifiers, fuzzy, MLP, K-NN. The use of a genetic algorithm is motivated by the fact that the combination phase is based on the optimization of vote strategy. The method has been applied to classification of The Pima Indians Diabetes database, results show a significant improvement of recognition accuracy using the genetic algorithm combination strategy compared with the recognition accuracy of each single classifier.

• バイオメディカル・ファジィ・システム学会の論文

著者

• Moradi Mohammad

  Biomedical Engineering Department Amir Kabir University Of Technology

• Adibzadeh Fatemeh

  Biomedical Engineering Department, Amir Kabir University of Technology

• Adibzadeh Fatemeh

  Biomedical Engineering Department Amir Kabir University Of Technology

関連論文

• Combination of Multiple Classifiers for Classifying the Diabetic Data(Biosensors: Data Acquisition, Processing and Control)

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