A Population Based Feature Subset Selection Algorithm Guided by Fuzzy Feature Dependency

Al-Ani, A; Khushaba, RN

A Population Based Feature Subset Selection Algorithm Guided by Fuzzy Feature Dependency

Al-Ani, A

Khushaba, RN

Permalink

Publication Type:: Conference Proceeding
Citation:: Communications in Computer and Information Science, 2012, 322 pp. 430 - 438
Issue Date:: 2012-01-01

Closed Access

	Filename	Description	Size
	2012001561OK.pdf	Published version	22.97 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	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.date.issued	2012-01-01	en_US
dc.identifier.citation	Communications in Computer and Information Science, 2012, 322 pp. 430 - 438	en_US
dc.identifier.isbn	9783642353253	en_US
dc.identifier.issn	1865-0929	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31915
dc.description.abstract	Population-based (or evolutionary) algorithms have been attracting an increased attention due to their powerful search capabilities. For the particular problem of feature selection, population-based methods aim to produce better "or fitter" future generations that contain more informative subsets of features. It is well-known that feature subset selection is a very challenging optimization problem, especially when dealing with datasets that contain large number of features. Most of the commonly used population-based feature selection methods use operators that do not take into account relationships between features to generate future subsets, which can have an impact on their capabilities to properly explore the search space. We present here a new population-based feature selection method that utilize dependency between features to guide the search. In addition, a novel method for estimating dependency between feature pairs is proposed based on the concept of fuzzy entropy. Results obtained from datasets with various sizes indicate the superiority of the proposed method in comparison to some of the well-known methods in the literature. © Springer-Verlag Berlin Heidelberg 2012.	en_US
dc.relation.ispartof	Communications in Computer and Information Science	en_US
dc.relation.isbasedon	10.1007/978-3-642-35326-0_43	en_US
dc.title	A Population Based Feature Subset Selection Algorithm Guided by Fuzzy Feature Dependency	en_US
dc.type	Conference Proceeding
utslib.citation.volume	322	en_US
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	Cairo, Egypt
pubs.embargo.period	Not known	en_US
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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	322	en_US

Abstract:

Population-based (or evolutionary) algorithms have been attracting an increased attention due to their powerful search capabilities. For the particular problem of feature selection, population-based methods aim to produce better "or fitter" future generations that contain more informative subsets of features. It is well-known that feature subset selection is a very challenging optimization problem, especially when dealing with datasets that contain large number of features. Most of the commonly used population-based feature selection methods use operators that do not take into account relationships between features to generate future subsets, which can have an impact on their capabilities to properly explore the search space. We present here a new population-based feature selection method that utilize dependency between features to guide the search. In addition, a novel method for estimating dependency between feature pairs is proposed based on the concept of fuzzy entropy. Results obtained from datasets with various sizes indicate the superiority of the proposed method in comparison to some of the well-known methods in the literature. © Springer-Verlag Berlin Heidelberg 2012.

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

http://hdl.handle.net/10453/31915