Floating search methods in feature selection
Pattern Recognition Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Data mining and genetic algorithm based gene/SNP selection
Artificial Intelligence in Medicine
k-nearest neighbors directed noise injection in multilayer perceptron training
IEEE Transactions on Neural Networks
Efficient training of RBF neural networks for pattern recognition
IEEE Transactions on Neural Networks
Using the patient's questionnaire data to screen laryngeal disorders
Computers in Biology and Medicine
Ensemble gene selection for cancer classification
Pattern Recognition
Review Article: Stable feature selection for biomarker discovery
Computational Biology and Chemistry
Recursive Mahalanobis Separability Measure for Gene Subset Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Robust Feature Selection for Microarray Data Based on Multicriterion Fusion
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Microarray gene expression data usually consist of a large amount of genes. Among these genes, only a small fraction is informative for performing cancer diagnostic test. This paper focuses on effective identification of informative genes. We analyze gene selection models from the perspective of optimization theory. As a result, a new strategy is designed to modify conventional search engines. Also, as overfitting is likely to occur in microarray data because of their small sample set, a point injection technique is developed to address the problem of overfitting. The proposed strategies have been evaluated on three kinds of cancer diagnosis. Our results show that the proposed strategies can improve the performance of gene selection substantially. The experimental results also indicate that the proposed methods are very robust under all the investigated cases.