Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The nature of statistical learning theory
The nature of statistical learning theory
Class prediction and discovery using gene expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Machine Learning
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Using learning to facilitate the evolution of features for recognizing visual concepts
Evolutionary Computation
Artificial immune system for classification of cancer
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Genetic algorithms for gene expression analysis
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Feature subset selection by genetic algorithms and estimation of distribution algorithms
Artificial Intelligence in Medicine
Extraction of informative genes from microarray data
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Feature selection for support vector machines with RBF kernel
Artificial Intelligence Review
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The analysis of large amount of gene expression profiles, which became available by rapidly developed monitoring tools, is an important task in bioinformatics. The problem we address is the discrimination of gene expression profiles of different classes, such as cancerous/benign tissues.Two subtasks in such problem, feature subset selection and inductive learning has critical effect on each other. In the wrapper' approach, combinatorial search of feature subset is done with performance of inductive learning as search criteria. This paper compares few combinations of supervised learning and combinatorial search when used in the wrapper approach. Also an extended GA implementation is introduced, which utilizes Clonal selection, a data-driven selection method. It compares very well to standard GA. The analysis of the obtained classifier reveals synergistic effect of genes in discrimination of the profiles.