Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Customer Retention via Data Mining
Artificial Intelligence Review - Issues on the application of data mining
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
International Journal of Hybrid Intelligent Systems
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Precise segmentation rendering for medical images based on maximum entropy processing
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
A combined neural network and decision trees model for prognosis of breast cancer relapse
Artificial Intelligence in Medicine
Attribute selection method based on a hybrid BPNN and PSO algorithms
Applied Soft Computing
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis
Computer Methods and Programs in Biomedicine
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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.