Universiti Teknologi Malaysia Institutional Repository

Filter-wrapper combination and embedded feature selection for gene expression data

Hameed, Shilan S. and Petinrin, Olutomilayo Olayemi and Hashi, Abdirahman Osman and Saeed, Faisal (2018) Filter-wrapper combination and embedded feature selection for gene expression data. International Journal of Advances in Soft Computing and its Applications, 10 (1). pp. 90-105. ISSN 2074-8523

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Abstract

Biomedical and bioinformatics datasets are generally large in terms of their number of features - and include redundant and irrelevant features, which affect the effectiveness and efficiency of classification of these datasets. Several different features selection methods have been utilised in various fields, including bioinformatics, to reduce the number of features. This study utilised Filter-Wrapper combination and embedded (LASSO) feature selection methods on both high and low dimensional datasets before classification was performed. The results illustrate that the combination of filter and wrapper feature selection to create a hybrid form of feature selection provides better performance than using filter only. In addition, LASSO performed better on high dimensional data.

Item Type:Article
Uncontrolled Keywords:Bioinformatics, Feature Selection
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:85805
Deposited By: Widya Wahid
Deposited On:28 Jul 2020 10:45
Last Modified:28 Jul 2020 10:45

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