NDLI: Analysis on the Drawbacks of the Commonly Used Measures of the Significance of Attributes in Decision-Table & A New Measure

Content Provider	IEEE Xplore Digital Library
Author	Decai Huang Lingli Wang
Copyright Year	2006
Description	Author affiliation: Coll. of Inf. Eng., Zhejiang Univ. of Technol., Hangzhou (Decai Huang; Lingli Wang)
Abstract	Reduction of knowledge is one of the important issues in the research of rough set theory. It has been proven that the search for an optimal (minimal) reduction of decision table is NP-hard problem. Thus people have been trying to search for more efficient heuristic algorithms to get an approximate reduction of decision table all the time. Every heuristic must depends on a measure of the significance of attributes in decision table, because it is the key heuristic information to determine which attribute is reducible in decision table. It was analyzed by examples in this paper that the current four commonly used methods all have some drawbacks. Using them to a heuristic leads to wrong results. Then a new definition for measuring the significance of attributes in decision table is given based on discernibility matrix. The new definition can discern the difference of significance of no-core attributes what cannot be discerned or misled by current commonly used methods. The new definition is very useful in the practice of data mining and reduction of knowledge
Sponsorship	STEMES/IMSCCS Conference, USA The University of Iowa, USA Zhejing Univ., China Harbin Eng. Univ., China
Starting Page	534
Ending Page	539
File Size	187741
Page Count	6
File Format	PDF
ISBN	0769525814
DOI	10.1109/IMSCCS.2006.190
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering Uncertainty NP-hard problem Heuristic algorithms Process control Machine learning Set theory Educational institutions Data mining Information analysis
Content Type	Text
Resource Type	Article

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