Neural Network Classifier with Entropy Based Feature Selection on Breast Cancer Diagnosis |
| |
Authors: | Mei-Ling Huang Yung-Hsiang Hung Wei-Yu Chen |
| |
Institution: | (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 |
| |
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. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|