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

Authors:
Zhiwang Zhang;Yong Shi;Peng Zhang;Guangxia Gao
Affiliations:
School of Information of Graduate University of Chinese Academy of Sciences, China/, Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100080, China;Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100080, China/ College of Information Science and Technology, University of Nebraska at Omaha, Omaha NE ...;School of Information of Graduate University of Chinese Academy of Sciences, China/, Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100080, China;Foreign Language Department, Shandong Institute of Business and Technology, Yantai, Shandong 264005, China
Venue:
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part II
Year:
2008

Citing 2
Cited 1

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Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
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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.