The nature of statistical learning theory
The nature of statistical learning theory
A comparison of genetic algorithms and other machine learning systems on a complex classification task from common disease research
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Feature Selection for Support Vector Machines by Means of Genetic Algorithms
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Finding predictive gene groups from microarray data
Journal of Multivariate Analysis
Comparative and Functional Genomics
Bounds on Error Expectation for Support Vector Machines
Neural Computation
Gene selection from microarray data for cancer classification-a machine learning approach
Computational Biology and Chemistry
Feature selection algorithms to find strong genes
Pattern Recognition Letters
Genetic algorithms for gene expression analysis
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Expert Systems with Applications: An International Journal
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Microarrays are emerging technologies that allow biologists to better understand the interactions between several pathologic states, at genes level. However, the amount of data generated by these tools becomes problematic when data are supposed to be automatically analyzed (e.g. for diagnostic purposes). In this work, the authors present a novel gene selection method based on Genetic Algorithms and Support Vector Machines (SVMs) for the classification of tissue samples. For such, the authors use an error estimate for SVMs to evaluate each individual's fitness. The proposed method is compared with common used gene selection techniques. Experimental results carried out using public available microarray datasets demonstrated the strength of the approach.