Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Application of neural networks and genetic algorithms in the classification of endothelial cells
Pattern Recognition Letters - special issue on pattern recognition in practice V
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Feature selection from huge feature sets in the context of computer vision
Feature selection from huge feature sets in the context of computer vision
Feature subset selection by genetic algorithms and estimation of distribution algorithms
Artificial Intelligence in Medicine
A genetic feature weighting scheme for pattern recognition
Integrated Computer-Aided Engineering
Ensemble Neural Networks with Novel Gene-Subsets for Multiclass Cancer Classification
Neural Information Processing
A General Framework of Feature Selection for Text Categorization
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A statistical-genetic algorithm to select the most significant features in mammograms
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
An efficient design of a nearest neighbor classifier for various-scale problems
Pattern Recognition Letters
Improving the Computational Efficiency of Recursive Cluster Elimination for Gene Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Engineering Applications of Artificial Intelligence
Environmentally realistic fingerprint-image generation with evolutionary filter-bank optimization
Expert Systems with Applications: An International Journal
DBNs-BLR (MCMC) -GAs-KNN: a novel framework of hybrid system for thalassemia expert system
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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With increasing interest in bioinformatics, sophisticated tools are required to efficiently analyze gene information. The classification of gene expression profiles is crucial in those fields. Since the features of data obtained by microarray technology come to be over thousands, it is essential to extract useful information by selecting proper features. The information without any feature selection might be redundant so that this can deteriorate the performance of classification. The conventional feature selection method with genetic algorithm has difficulty for huge-scale feature selection. In this paper, we modify the representation of chromosome to be suitable for huge-scale feature selection and adopt speciation to enhance the performance of feature selection by obtaining diverse solutions. Experimental results with DNA microarray data from cancer patients show that the selected genes by the proposed method are useful for cancer classification.