A Biological Data-Driven Mining Technique by Using Hybrid Classifiers With Rough Set

Chowdhury, L; Kamal, MS; Ripon, SH; Parvin, S; Hussain, OK; Ashour, A; Chowdhury, BR

A Biological Data-Driven Mining Technique by Using Hybrid Classifiers With Rough Set

Chowdhury, L Kamal, MS Ripon, SH Parvin, S Hussain, OK Ashour, A Chowdhury, BR

Permalink

Publisher:: IGI Global
Publication Type:: Journal Article
Citation:: International Journal of Ambient Computing and Intelligence, 2021, 12, (3), pp. 123-139
Issue Date:: 2021-07-01

Closed Access

	Filename	Description	Size
	A-Biological-Data-Driven-Mining-Technique-by-Using-Hybrid-Classifiers-With-Rough-Set.pdf	Published version	1.65 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Chowdhury, L
dc.contributor.author	Kamal, MS
dc.contributor.author	Ripon, SH
dc.contributor.author	Parvin, S
dc.contributor.author	Hussain, OK
dc.contributor.author	Ashour, A
dc.contributor.author	Chowdhury, BR
dc.date.accessioned	2022-05-12T04:35:41Z
dc.date.available	2022-05-12T04:35:41Z
dc.date.issued	2021-07-01
dc.identifier.citation	International Journal of Ambient Computing and Intelligence, 2021, 12, (3), pp. 123-139
dc.identifier.issn	1941-6237
dc.identifier.issn	1941-6245
dc.identifier.uri	http://hdl.handle.net/10453/157280
dc.description.abstract	<jats:p>Biological data classification and analysis are significant for living organs. A biological data classification is an approach that classifies the organs into a particular group based on their features and characteristics. The objective of this paper is to establish a hybrid approach with naive Bayes, apriori algorithm, and KNN classifier that generates optimal classification rules for finding biological pattern matching. The authors create combined association rules by using naïve Bayes and apriori approach with a rough set for next sequence prediction. First, the large DNA sequence is reduced by using k-nearest approach. They apply association rules by using naïve Bayes and apriori approach for the next sequence pattern. The hybrid approach provides more accuracy than single classifier for biological sequence prediction. The optimized hybrid process needs less execution time for rule generation for massive biological data analysis. The results established that the hybrid approach generally outperforms the other association rule generation approach.</jats:p>
dc.language	en
dc.publisher	IGI Global
dc.relation.ispartof	International Journal of Ambient Computing and Intelligence
dc.relation.isbasedon	10.4018/ijaci.2021070106
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0806 Information Systems, 1902 Film, Television and Digital Media
dc.title	A Biological Data-Driven Mining Technique by Using Hybrid Classifiers With Rough Set
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
utslib.for	1902 Film, Television and Digital Media
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 Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-12T04:35:39Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	3

Abstract:

Biological data classification and analysis are significant for living organs. A biological data classification is an approach that classifies the organs into a particular group based on their features and characteristics. The objective of this paper is to establish a hybrid approach with naive Bayes, apriori algorithm, and KNN classifier that generates optimal classification rules for finding biological pattern matching. The authors create combined association rules by using naïve Bayes and apriori approach with a rough set for next sequence prediction. First, the large DNA sequence is reduced by using k-nearest approach. They apply association rules by using naïve Bayes and apriori approach for the next sequence pattern. The hybrid approach provides more accuracy than single classifier for biological sequence prediction. The optimized hybrid process needs less execution time for rule generation for massive biological data analysis. The results established that the hybrid approach generally outperforms the other association rule generation approach.

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

http://hdl.handle.net/10453/157280