Combination of Reduction Detection Using TOPSIS for Gene Expression Data Analysis

Tripathy, J; Dash, R; Pattanayak, BK; Mishra, SK; Mishra, TK; Puthal, D

Combination of Reduction Detection Using TOPSIS for Gene Expression Data Analysis

Tripathy, J Dash, R Pattanayak, BK Mishra, SK Mishra, TK Puthal, D

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Big Data and Cognitive Computing, 2022, 6, (1), pp. 24
Issue Date:: 2022-03-01

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Field	Value	Language
dc.contributor.author	Tripathy, J
dc.contributor.author	Dash, R
dc.contributor.author	Pattanayak, BK
dc.contributor.author	Mishra, SK
dc.contributor.author	Mishra, TK
dc.contributor.author	Puthal, D https://orcid.org/0000-0002-8332-278X
dc.date.accessioned	2023-03-21T00:32:31Z
dc.date.available	2023-03-21T00:32:31Z
dc.date.issued	2022-03-01
dc.identifier.citation	Big Data and Cognitive Computing, 2022, 6, (1), pp. 24
dc.identifier.issn	2504-2289
dc.identifier.issn	2504-2289
dc.identifier.uri	http://hdl.handle.net/10453/167850
dc.description.abstract	In high-dimensional data analysis, Feature Selection (FS) is one of the most fundamental issues in machine learning and requires the attention of researchers. These datasets are characterized by huge space due to a high number of features, out of which only a few are significant for analysis. Thus, significant feature extraction is crucial. There are various techniques available for feature selection; among them, the filter techniques are significant in this community, as they can be used with any type of learning algorithm and drastically lower the running time of optimization algorithms and improve the performance of the model. Furthermore, the application of a filter approach depends on the characteristics of the dataset as well as on the machine learning model. Thus, to avoid these issues in this research, a combination of feature reduction (CFR) is considered designing a pipeline of filter approaches for high-dimensional microarray data classification. Considering four filter approaches, sixteen combinations of pipelines are generated. The feature subset is reduced in different levels, and ultimately, the significant feature set is evaluated. The pipelined filter techniques are Correlation-Based Feature Selection (CBFS), Chi-Square Test (CST), Information Gain (InG), and Relief Feature Selection (RFS), and the classification techniques are Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and k-Nearest Neighbor (k-NN). The performance of CFR depends highly on the datasets as well as on the classifiers. Thereafter, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used for ranking all reduction combinations and evaluating the superior filter combination among all.
dc.language	en
dc.publisher	MDPI
dc.relation.ispartof	Big Data and Cognitive Computing
dc.relation.isbasedon	10.3390/bdcc6010024
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Combination of Reduction Detection Using TOPSIS for Gene Expression Data Analysis
dc.type	Journal Article
utslib.citation.volume	6
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-21T00:32:30Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	6
utslib.citation.issue	1

Abstract:

In high-dimensional data analysis, Feature Selection (FS) is one of the most fundamental issues in machine learning and requires the attention of researchers. These datasets are characterized by huge space due to a high number of features, out of which only a few are significant for analysis. Thus, significant feature extraction is crucial. There are various techniques available for feature selection; among them, the filter techniques are significant in this community, as they can be used with any type of learning algorithm and drastically lower the running time of optimization algorithms and improve the performance of the model. Furthermore, the application of a filter approach depends on the characteristics of the dataset as well as on the machine learning model. Thus, to avoid these issues in this research, a combination of feature reduction (CFR) is considered designing a pipeline of filter approaches for high-dimensional microarray data classification. Considering four filter approaches, sixteen combinations of pipelines are generated. The feature subset is reduced in different levels, and ultimately, the significant feature set is evaluated. The pipelined filter techniques are Correlation-Based Feature Selection (CBFS), Chi-Square Test (CST), Information Gain (InG), and Relief Feature Selection (RFS), and the classification techniques are Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and k-Nearest Neighbor (k-NN). The performance of CFR depends highly on the datasets as well as on the classifiers. Thereafter, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used for ranking all reduction combinations and evaluating the superior filter combination among all.

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http://hdl.handle.net/10453/167850