Framework for Feature Selection in Health Assessment Systems

Ubaid, A; Dong, F; Hussain, FK

Framework for Feature Selection in Health Assessment Systems

Ubaid, A

Dong, F Hussain, FK

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Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2020, 926 pp. 313 - 324
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Ubaid, A https://orcid.org/0000-0002-9833-9891	en_US
dc.contributor.author	Dong, F	en_US
dc.contributor.author	Hussain, FK https://orcid.org/0000-0003-1513-8072	en_US
dc.date.issued	2020-01-01	en_US
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2020, 926 pp. 313 - 324	en_US
dc.identifier.isbn	9783030150310	en_US
dc.identifier.issn	2194-5357	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137346
dc.description.abstract	© 2020, Springer Nature Switzerland AG. Anomaly detection in health assessment systems has gained much attention in the recent past. Various feature selection techniques have been proposed for successful anomaly detection. However, these methods do not cater for the need to select features in health assessment systems. Most of the present techniques are data dependent and do not offer an option for incorporating domain information. This paper proposes a novel domain knowledge-driven feature selection framework named domain-driven selective wrapping (DSW) that can help in the selection of a correlated feature subset. The proposed framework uses an expert’s domain knowledge for the selection of subsets. The framework uses a custom-designed logic-driven anomaly detection block (LDAB) as a wrapper. The experiment results show that the proposed framework is able to select feature subsets more efficiently than traditional sequential selection methods and is very successful in detecting anomalies.	en_US
dc.relation.ispartof	Advances in Intelligent Systems and Computing	en_US
dc.relation.isbasedon	10.1007/978-3-030-15032-7_27	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Framework for Feature Selection in Health Assessment Systems	en_US
dc.type	Conference Proceeding
utslib.citation.volume	926	en_US
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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	926	en_US

Abstract:

© 2020, Springer Nature Switzerland AG. Anomaly detection in health assessment systems has gained much attention in the recent past. Various feature selection techniques have been proposed for successful anomaly detection. However, these methods do not cater for the need to select features in health assessment systems. Most of the present techniques are data dependent and do not offer an option for incorporating domain information. This paper proposes a novel domain knowledge-driven feature selection framework named domain-driven selective wrapping (DSW) that can help in the selection of a correlated feature subset. The proposed framework uses an expert’s domain knowledge for the selection of subsets. The framework uses a custom-designed logic-driven anomaly detection block (LDAB) as a wrapper. The experiment results show that the proposed framework is able to select feature subsets more efficiently than traditional sequential selection methods and is very successful in detecting anomalies.

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http://hdl.handle.net/10453/137344