A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization

This paper proposes an approach for gene selection in microarray data. The proposed approach consists of a primary filter approach using Fisher criterion which reduces the initial genes and hence the search space and time complexity. Then, a wrapper approach which is based on cellular learning autom...

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Vydáno v:	Genomics (San Diego, Calif.) Ročník 107; číslo 6; s. 231 - 238
Hlavní autoři:	Vafaee Sharbaf, Fatemeh, Mosafer, Sara, Moattar, Mohammad Hossein
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Inc 01.06.2016
Témata:	Algorithms ant colonies Ant colony optimization Cellular learning automata Cluster Analysis data collection Data Interpretation, Statistical Formicidae Gene selection genes Humans hybrids K-nearest neighbor Machine Learning Microarray data microarray technology Naïve Bayes Oligonucleotide Array Sequence Analysis - methods Oligonucleotide Array Sequence Analysis - statistics & numerical data space and time Support Vector Machine support vector machines system optimization Ant colony optimization Naïve Bayes Gene selection Microarray data Cellular learning automata K-nearest neighbor
ISSN:	0888-7543, 1089-8646
On-line přístup:	Získat plný text
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