Journal
JOURNAL OF MEDICAL SYSTEMS
Volume 34, Issue 5, Pages 865-873Publisher
SPRINGER
DOI: 10.1007/s10916-009-9301-x
Keywords
Feature selection; Back-propagation neural network (BPNN); Classification accuracy; Levenberg-Marquardt (LM); Particle Swarm Optimization (PSO)
Funding
- National Science Council of the Republic of China [NSC 96-2221-E-167-001]
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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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