A Rough Set-Based Multiple Criteria Linear Programming Approach for Classification

Zhang, Z; Shi, Y; Zhang, P; Gao, G

A Rough Set-Based Multiple Criteria Linear Programming Approach for Classification

Zhang, Z Shi, Y Zhang, P

Gao, G

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science, 2008, 5102 (1), pp. 476 - 485
Issue Date:: 2008-01

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Field	Value	Language
dc.contributor.author	Zhang, Z	en_US
dc.contributor.author	Shi, Y	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Gao, G	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science, 2008, 5102 (1), pp. 476 - 485	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28909
dc.description.abstract	It is well known that data mining is a process of discovering unknown, hidden information from a large amount of data, extracting valuable information, and using the information to make important business decisions. And data mining has been developed into a new information technology, including regression, decision tree, neural network, fuzzy set, rough set, support vector machine and so on. This paper puts forward a rough set-based multiple criteria linear programming (RS-MCLP) approach for solving classification problems in data mining. Firstly, we describe the basic theory and models of rough set and multiple criteria linear programming (MCLP) and analyse their characteristics and advantages in practical applications. Secondly, detailed analysis about their deficiencies are provided respectively. However, because of the existing mutual complementarities between them, we put forward and build the RS-MCLP methods and models which sufficiently integrate their virtues and overcome the adverse factors simultaneously. In addition, we also develop and implement these algorithm and models in SAS and Windows platform. Finally, many experiments show that RS-MCLP approach is prior to single MCLP model and other traditional classification methods in data mining.	en_US
dc.publisher	Elsevier	en_US
dc.relation.ispartof	Lecture Notes in Computer Science	en_US
dc.relation.isbasedon	10.1007/978-3-540-69387-1_54	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A Rough Set-Based Multiple Criteria Linear Programming Approach for Classification	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	5102	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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	5102	en_US

Abstract:

It is well known that data mining is a process of discovering unknown, hidden information from a large amount of data, extracting valuable information, and using the information to make important business decisions. And data mining has been developed into a new information technology, including regression, decision tree, neural network, fuzzy set, rough set, support vector machine and so on. This paper puts forward a rough set-based multiple criteria linear programming (RS-MCLP) approach for solving classification problems in data mining. Firstly, we describe the basic theory and models of rough set and multiple criteria linear programming (MCLP) and analyse their characteristics and advantages in practical applications. Secondly, detailed analysis about their deficiencies are provided respectively. However, because of the existing mutual complementarities between them, we put forward and build the RS-MCLP methods and models which sufficiently integrate their virtues and overcome the adverse factors simultaneously. In addition, we also develop and implement these algorithm and models in SAS and Windows platform. Finally, many experiments show that RS-MCLP approach is prior to single MCLP model and other traditional classification methods in data mining.

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http://hdl.handle.net/10453/28909