Similarity model based on CBR and FCA

Shi, C; Lai, L; Fan, J; Bai, Y

Similarity model based on CBR and FCA

Shi, C Lai, L Fan, J Bai, Y

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2016 17th IEEE/ACIS International Conference on, 2016
Issue Date:: 2016

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Field	Value	Language
dc.contributor.author	Shi, C	en_US
dc.contributor.author	Lai, L	en_US
dc.contributor.author	Fan, J	en_US
dc.contributor.author	Bai, Y	en_US
dc.date	2016-05-30	en_US
dc.date.issued	2016	en_US
dc.identifier.citation	Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2016 17th IEEE/ACIS International Conference on, 2016	en_US
dc.identifier.isbn	978-1-5090-2239-7	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122910
dc.description.abstract	Case-based reasoning (CBR) is one of the research highlights in the artificial intelligence field. In the process of case retrieval of CBR, Similarity is an important index in evaluation. This paper proposed a new model calculating similarity between source case and target case. The model is suitable for using Formal Concept Analysis (FCA) in the case of CBR. The model considered the case attributes weights between two formal concepts and feature attributes weights in concept lattices. Comparing to the similarity model put forward by Jirapond Tadrat, this model cut down the comparison to the objects, weight more in the attributes and shorten the time of solving the similarity. Theoretical deduction proves that the proposed similarity model satisfy the basic conditions which all these models need to meet. This article chose the UCI data sets and the method of cross validation, carried on an experiment from both similarity model aspect and classifier aspect respectively. The former experimental results show that the similarity model has higher accuracy than others. The latter experimental results show that the similarity model of CBR classifier has higher accuracy in the attribute set density compared with other small data set classifier.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2016 17th IEEE/ACIS International Conference on	en_US
dc.relation.ispartof	IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing	en_US
dc.relation.isbasedon	10.1109/SNPD.2016.7515965	en_US
dc.rights	© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Similarity model based on CBR and FCA	en_US
dc.type	Conference Proceeding
utslib.location.activity	Shanghai, China	en_US
utslib.for	0802 Computation Theory and Mathematics	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/Students
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2016-06-01	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2016-05-30	en_US

Abstract:

Case-based reasoning (CBR) is one of the research highlights in the artificial intelligence field. In the process of case retrieval of CBR, Similarity is an important index in evaluation. This paper proposed a new model calculating similarity between source case and target case. The model is suitable for using Formal Concept Analysis (FCA) in the case of CBR. The model considered the case attributes weights between two formal concepts and feature attributes weights in concept lattices. Comparing to the similarity model put forward by Jirapond Tadrat, this model cut down the comparison to the objects, weight more in the attributes and shorten the time of solving the similarity. Theoretical deduction proves that the proposed similarity model satisfy the basic conditions which all these models need to meet. This article chose the UCI data sets and the method of cross validation, carried on an experiment from both similarity model aspect and classifier aspect respectively. The former experimental results show that the similarity model has higher accuracy than others. The latter experimental results show that the similarity model of CBR classifier has higher accuracy in the attribute set density compared with other small data set classifier.

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http://hdl.handle.net/10453/122910