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Neural Network Classifier with Entropy Based Feature Selection on Breast Cancer Diagnosis

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Abstract

The aim of this research is to combine the feature selection (FS) and optimization algorithms as the optimal tool to improve the learning performance like predictive accuracy of the Wisconsin Breast Cancer Dataset classification. An ensemble of the reduced data patterns based on FS was used to train a neural network (NN) using the Levenberg–Marquardt (LM) and the Particle Swarm Optimization (PSO) algorithms to devise the appropriate NN training weighting parameters, and then construct an effective Neural Network classifier to improve the Wisconsin Breast Cancers’ classification accuracy and efficiency. Experimental results show that the accuracy and AROC improved emphatically, and the best performance in accuracy and AROC are 98.83% and 0.9971, respectively.

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Acknowledgements

The authors would like to thank the anonymous referees for their careful reading of the paper and for making several suggestions that improved it. The authors would also like to thank the National Science Council of the Republic of China for financially supporting this research under contract NSC 96-2221-E-167-001.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, National Chin-Yi University of Technology, 35, Lane 215, Section 1, Chung-San Rd., Taiping, Taichung, 411, Taiwan, Republic of China

    Mei-Ling Huang, Yung-Hsiang Hung & Wei-Yu Chen

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  1. Mei-Ling Huang
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Correspondence to Yung-Hsiang Hung.

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Huang, ML., Hung, YH. & Chen, WY. Neural Network Classifier with Entropy Based Feature Selection on Breast Cancer Diagnosis. J Med Syst 34, 865–873 (2010). https://doi.org/10.1007/s10916-009-9301-x

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