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Vol: 52(66) No: 4 / December 2007 

Visualizing a Genetic Algorithm - Support Vector Machine Approach to Gene Microarrays Supervised Learning
Nicolae Teodor Melita
Computer Science and Engineering Department, \"Politehnica\" University of Timisoara, Faculty of Automation and Computers, Bd. V. Parvan 2, RO-300223 Timisoara, Romania, e-mail: nt_melita@yahoo.com
Stefan Holban
Computer Science and Engineering Department, \"Politehnica\" University of Timisoara, Faculty of Automation and Computers, Bd. V. Parvan 2, RO-300223 Timisoara, Romania, phone: +40256404060, e-mail: stefan@cs.utt.ro, web: http://www.cs.utt.ro/~stefan/


Keywords: Support Vector Machines, Genetic Algorithm, Feature Selection, DNA microarrays, Visualization Tools

Abstract
We address the problem of collecting and analyzing vast amount of information in medicine and biology, in the light of the revolutionary technological evolution in the last decades. Currently, the methods of achieving information overcome our capacity to sort and process that information. However, we use the methods of machine learning to sort and analyze this information. In this comprehensive review we describe an experiment of analyzing DNA microarrays using Support Vector Machines (SVM). We study how the SVM performs in classifying three instances of the same dataset. We classify the brute dataset, a t-test based filtered dataset, and a dataset with features selected by a Genetic Algorithm (GA). We emphasize on the methods to visualize and present the results, given the fact that such a study provides usually tools for multidisciplinary research teams.

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