A roughness measure for fuzzy sets

Huynh, Van–Nam; Nakamori, Yoshiteru

doi:10.1016/j.ins.2004.07.017

Cited by 56 publications

(24 citation statements)

References 23 publications

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“…To evaluate the decision performance, certainty measure and support measure were introduced to evaluate a single decision rule and were not suitable for measuring a rule set [3,14]. For a rule set, two measures were introduced to measure the certainty and consistency in [32].…”

Section: Efficiency Analysismentioning

confidence: 99%

An efficient rough feature selection algorithm with a multi-granulation view

Liang

Wang

Dang

et al. 2012

International Journal of Approximate Reasoning

162

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Feature selection is a challenging problem in many areas such as pattern recognition, machine learning and data mining. Rough set theory, as a valid soft computing tool to analyze various types of data, has been widely applied to select helpful features (also called attribute reduction). In rough set theory, many feature selection algorithms have been developed in the literatures, however, they are very time-consuming when data sets are in a large scale. To overcome this limitation, we propose in this paper an efficient rough feature selection algorithm for large-scale data sets, which is stimulated from multi-granulation. A sub-table of a data set can be considered as a small granularity. Given a large-scale data set, the algorithm first selects different small granularities and then estimate on each small granularity the reduct of the original data set. Fusing all of the estimates on small granularities together, the algorithm can get an approximate reduct. Because of that the total time spent on computing reducts for sub-tables is much less than that for the original large-scale one, the algorithm yields in a much less amount of time a feature subset (the approximate reduct). According to several decision performance measures, experimental results show that the proposed algorithm is feasible and efficient for large-scale data sets.

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Section: Efficiency Analysismentioning

confidence: 99%

An efficient rough feature selection algorithm with a multi-granulation view

Liang

Wang

Dang

et al. 2012

International Journal of Approximate Reasoning

162

View full text Add to dashboard Cite

show abstract

“…Here, we adopt rough sets to approximate the uncertain fuzzy membership function of a fuzzy set. Rough sets provide a different model in representing ambiguity of sets, and the combination of fuzzy sets with rough sets has received intensive investigations [4,10,[14][15][16][18][19][20][21]23,27,28,[31][32][33][34]38,39,41,50,51]. However, most works focus on the fuzzy equivalence relations.…”

Section: Introductionmentioning

confidence: 99%

A new extension of fuzzy sets using rough sets: R-fuzzy sets

Yang

Hinde

2010

Information Sciences

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• This is the author's version of a work that was accepted for publication in the journal Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published at:

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“…The study by Banerjee et al strengthens the connections between rough sets and fuzzy sets with the roughness measure of fuzzy sets [10]. Huynh et al introduced a new roughness measure of fuzzy sets based on mass assignment [11]. The roughness of a fuzzy set was then interpreted as the weighted sum of the roughness measures of nested focal subsets.…”

Section: Introductionmentioning

confidence: 99%

“…The results show that objects are extracted with higher accuracy using their approach, compared to Shannon's probabilistic entropy. In the work of Huynh et al [11], a new roughness measure was used to analyze relational databases. Yang et al reported that roughness measures are important indicators for decision making applications [12].…”

Section: Introductionmentioning

confidence: 99%