Title Of Paper:
 
 
Improving Performance of Cancer Automatic Recognition Systems by Selecting Effective Features Based on Synergy of Genetic Algorithm and Learning Automata
Author's Name :  Bahare Kadkhodazadeh, Mohammad Mosleh, Mohammad Kheirandish
KeyWords:  DNA microarray, Feature selection, Learning automata, Genetic algorithm, Cancer classification
Pages:  9 -21
Volume: 5
Issue: 1
Year: 2017
Abstract:
 

In world today, recognizing cancer by using DNA micro arrays is a challenge which has engaged minds of many of researchers, amongst these challenges there are the large number of genes that exist in each sample and the irrelevant genes and the extension and existing of noise in databases which results in of classifier accuracy. In this paper a method has been presented which is able to increase the of recognizer by selecting effective features based on synergy of genetic algorithm and learning automata. In order to do the classification, the support vector machine has been used. Also it has been tested in order to evaluate the accuracy of the suggested method on Colon, Promoters, Lung and Prostate datasets. The achieved results show the of the suggested method compared to other methods.

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