A Novel Unsupervised Feature Selection Approach Using Genetic Algorithm on Partitioned Data

Saxena, A; Chugh, D; Mittal, H; Sajid, M; Chauhan, R; Yafi, E; Cao, J; Prasad, M

A Novel Unsupervised Feature Selection Approach Using Genetic Algorithm on Partitioned Data

Saxena, A Chugh, D Mittal, H Sajid, M Chauhan, R Yafi, E Cao, J Prasad, M

Permalink

Publisher:: Advances in Artificial Intelligence and Machine Learning
Publication Type:: Journal Article
Citation:: Advances in Artificial Intelligence and Machine Learning, 2022, 02, (04), pp. 500-515
Issue Date:: 2022

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (166.43 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Saxena, A
dc.contributor.author	Chugh, D
dc.contributor.author	Mittal, H
dc.contributor.author	Sajid, M
dc.contributor.author	Chauhan, R
dc.contributor.author	Yafi, E
dc.contributor.author	Cao, J
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.date.accessioned	2023-03-24T10:14:09Z
dc.date.available	2023-03-24T10:14:09Z
dc.date.issued	2022
dc.identifier.citation	Advances in Artificial Intelligence and Machine Learning, 2022, 02, (04), pp. 500-515
dc.identifier.issn	2582-9793
dc.identifier.issn	2582-9793
dc.identifier.uri	http://hdl.handle.net/10453/168367
dc.description.abstract	<jats:p>A novel feature selection approach is presented in this paper. Sammon’s Stress Function transforms the high dimension data to a lower dimension data set. A data set is divided into small partitions. The features are assigned randomly to these partitions. Using GA with Sammon Error as fitness value, a small, desired number of features are selected from every partition. The combination of the reduced subsets of the features from these partitions is again divided into small partitions. After a certain number of iterating the process, a desired small number of features is obtained. For experimental validation, the proposed method has been tested on 11 standard datasets with three classifiers namely, Decision Tree, MLP and KNN. The classification accuracies obtained by the proposed method is highest on most of the considered datasets against the results reported in literature. Moreover, the proposed method selects comparatively less number of features in comparison to considered methods. The optimistic results obtained from the proposed method justify its strength.</jats:p>
dc.language	en
dc.publisher	Advances in Artificial Intelligence and Machine Learning
dc.relation.ispartof	Advances in Artificial Intelligence and Machine Learning
dc.relation.isbasedon	10.54364/aaiml.2022.1134
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Novel Unsupervised Feature Selection Approach Using Genetic Algorithm on Partitioned Data
dc.type	Journal Article
utslib.citation.volume	02
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-24T10:14:08Z
pubs.issue	04
pubs.publication-status	Published
pubs.volume	02
utslib.citation.issue	04

Abstract:

A novel feature selection approach is presented in this paper. Sammon’s Stress Function transforms the high dimension data to a lower dimension data set. A data set is divided into small partitions. The features are assigned randomly to these partitions. Using GA with Sammon Error as fitness value, a small, desired number of features are selected from every partition. The combination of the reduced subsets of the features from these partitions is again divided into small partitions. After a certain number of iterating the process, a desired small number of features is obtained. For experimental validation, the proposed method has been tested on 11 standard datasets with three classifiers namely, Decision Tree, MLP and KNN. The classification accuracies obtained by the proposed method is highest on most of the considered datasets against the results reported in literature. Moreover, the proposed method selects comparatively less number of features in comparison to considered methods. The optimistic results obtained from the proposed method justify its strength.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/168367