Call For Paper Volume:4 Issue:10 Oct'2017 |

GS-RSAR : A Technique for Feature Selection of Microarray Data

Publication Date : 30/04/2015



Author(s) :

Bichitrananda Patra.


Volume/Issue :
Volume 2
,
Issue 4
(04 - 2015)



Abstract :

Gene expression profiles have great potential as a medical diagnostic tool since they represent the state of a cell at the molecular level. Available training data sets for classification of cancer types generally have a fairly small sample size compared to the number of genes involved. This fact poses an insurmountable problem to some classification methodologies due to training data limitations. Feature selection is considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable classification accuracy. Hence, selecting relevant genes from the microarray data poses a formidable challenge to researchers due to the high-dimensionality of features, multi-class categories being involved, and the usually small sample size. To overcome this difficulty, a good selection method for genes relevant for sample classification is needed in order to improve prediction accuracy, and to avoid incomprehensibility due to the large number of genes investigated. In this paper, irrelevant genes are eliminated in two stages, employing   correlation-based feature selection (CFS)as an evaluator and  genetic search (GS)as a search technique  at the first phase and in the second phase of elimination it an implementation of the Quick reduct algorithm of rough set attribute reduction(RSAR) and in third phase of elimination is (CFS+GS) combines Quick-Reduct algorithm  and forms an  integrated filter method. Since the data consist of a large number of redundant features, an initial redundancy reduction of the gene is done to enable faster convergence. Then Rough set theory is employed to generate reducts, which represent the minimal sets of non-redundant gene capable of discerning between all objects, in a multi-objective framework. The effectiveness of the proposed approach was verified on  six different binary and multi-class microarray datasets using four different ANN classifier as LVQ1, LVQ2, OLVQ1 and SOM  with 10 fold cross-validation method.


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GS-RSAR : A Technique for Feature Selection of Microarray Data

April 29, 2015