Please Scroll down for Article International Journal of General Systems Comparative Study of Decision Performance of Decision Tables Induced by Attribute Reductions Comparative Study of Decision Performance of Decision Tables Induced by Attribute Reductio
Please Scroll down for Article International Journal of General Systems Comparative Study of Decision Performance of Decision Tables Induced by Attribute Reductions Comparative Study of Decision Performance of Decision Tables Induced by Attribute Reductio
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通讯作者：
W Wei;Wei Wei-Wei;Jiye Liang;Y. Qian;Feng Wang;C. Dang;Hong Kong;Wei Liang;Wang;Feng
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作者：
W Wei;Wei Wei-Wei;Jiye Liang;Y. Qian;Feng Wang;C. Dang;Hong Kong;Wei Liang;Wang;Feng
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This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. The given attribute reduction approach decides the decision performance of a reduced decision table, which can give a guidance for selecting one rule-extraction method in practical applications. The objective of this study is to compare the decision performance of positive-region reduction, Shannon entropy reduction and Liang entropy reduction. In this paper, the relationships between positive-region reduction, Shannon entropy reduction and Liang entropy reduction are first investigated. Then, by means of three evaluation indices (certainty measure, consistency measure and support measure), we systemically analyse these change mechanisms for decision performance of a decision table induced by each of these three types of reduction approaches. Finally, by numerical experiments, these change mechanisms of a decision table's decision performance are verified for the above-mentioned three attribute reductions. 1. Introduction Rough set theory was proposed by Pawlak in 1982. Recently, it has become a popular mathematical framework for pattern recognition, image processing, feature selection, neuro computing, conflict analysis, decision support, data mining and knowledge discovery processing from large data-sets In recent years, more attention has been paid to attribute reduction in information systems and decision tables. Many types of attribute-reduction techniques have been For our development, we briefly recall some of these techniques. Skowron and Rauszer (1992) proposed an attribute-reduction algorithm using a discernibility matrix, which can find all reducts. However, it only works in small data-sets because the algorithm is very time consuming.