Neural network classifier with entropy based feature selection on breast cancer diagnosis.
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.
Huang
Mei-Ling
ML
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. huangml@ncut.edu.tw
Hung
Yung-Hsiang
YH
Chen
Wei-Yu
WY
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000473:pathology*; D057225:Data Mining; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation*; D005260:Female; D006801:Humans; D016571:Neural Networks, Computer