A subset selection method using Filter and wrapper algorithms based on the nature of the expression values in microarray data sets for gene feature selection

Azad, Tamzid; Islam, Md. Mazharul

dc.contributor.author	Azad, Tamzid
dc.contributor.author	Islam, Md. Mazharul
dc.date.accessioned	2021-09-16T05:04:21Z
dc.date.available	2021-09-16T05:04:21Z
dc.date.issued	2014-11-15
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dc.identifier.uri	http://hdl.handle.net/123456789/1001
dc.description	Supervised by Prof. Dr. M. A. Mottalib, Head of the Department, Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh.	en_US
dc.description.abstract	In the field of micro-array data analysis the crucial first step is gene selection. The process refers to selecting a subset consisting of a few genes which are of genetic significance out of thousands of genes to make the job of the classifier algorithm computationally easy and efficient at the same time. Feature selection plays an important role in classification. The first set of data are gene expression profiles from Acute Lymphoblastic Leukemia (ALL) patients. In this paper an algorithm is proposed for feature subset selection (FFS) which is based on the nature of intensity values in microarray datasets. The proposed method is a combination of a filter and a wrapper algorithm which selects subsets. It is based on two assumptions. Our results demonstrate the importance of feature selection in accurately classifying new samples	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.subject	Gene expression profiles, Feature selection, Classification, Filter, Wrapper, Weka	en_US
dc.title	A subset selection method using Filter and wrapper algorithms based on the nature of the expression values in microarray data sets for gene feature selection	en_US
dc.type	Thesis	en_US