Knowledge extraction from multiple criteria linear programming classification approach

Zhang, Y; Zhang, P; Zhang, L; Shi, Y

Knowledge extraction from multiple criteria linear programming classification approach

Zhang, Y Zhang, P

Zhang, L Shi, Y

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Procedia Computer Science, 2010, 1 (1), pp. 2441 - 2448
Issue Date:: 2010-01

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Field	Value	Language
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Shi, Y	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	Procedia Computer Science, 2010, 1 (1), pp. 2441 - 2448	en_US
dc.identifier.issn	1877-0509	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28903
dc.description.abstract	As an effective model for classification, Multiple Criteria Linear Programming (MCLP) has been widely used in business intelligence. However, a possible limitation of MCLP is that it generates unexplainable black-box models which can only tell us results without reasons. To overcome this shortage, in this paper, we present a knowledge mining strategy which mines explainable decision rules from black-box MCLP models. Firstly, we use the rough set theory to distinguish the definable set where samples are perfectly classified, from the rough set where misclassified samples may exist. Then, to get explainable knowledge, we present a clustering-based decision rule extraction approach to extract knowledge from the definable set, and a rough set-based rule extraction approach to the rough set. Finally, empirical studies on real world VIP Email data sets demonstrate that our method can effectively extract explicit rules from MCLP model with only a little lost in performance.	en_US
dc.publisher	Elsevier	en_US
dc.relation.ispartof	Procedia Computer Science	en_US
dc.relation.isbasedon	10.1016/j.procs.2010.04.275	en_US
dc.title	Knowledge extraction from multiple criteria linear programming classification approach	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	10 Technology	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
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	1	en_US
pubs.volume	1	en_US

Abstract:

As an effective model for classification, Multiple Criteria Linear Programming (MCLP) has been widely used in business intelligence. However, a possible limitation of MCLP is that it generates unexplainable black-box models which can only tell us results without reasons. To overcome this shortage, in this paper, we present a knowledge mining strategy which mines explainable decision rules from black-box MCLP models. Firstly, we use the rough set theory to distinguish the definable set where samples are perfectly classified, from the rough set where misclassified samples may exist. Then, to get explainable knowledge, we present a clustering-based decision rule extraction approach to extract knowledge from the definable set, and a rough set-based rule extraction approach to the rough set. Finally, empirical studies on real world VIP Email data sets demonstrate that our method can effectively extract explicit rules from MCLP model with only a little lost in performance.

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http://hdl.handle.net/10453/28903