Rough set model based on possibility measure

Rough sets theory (RS) is a new tool for processing fuzzy and uncertain knowledge, and has already been applied to many areas successfully. In this paper, by analyzing the basic characteristic of rough set, for the deficiency that the existing rough set model can't effectively solve data reduct...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 5; pp. 2657 - 2662
Main Authors Fa-Chao Li, Li-Na An
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Approximate operators
Artificial intelligence
Educational institutions
Evidence theory
Fuzzy set theory
Information processing
Machine learning
Pattern recognition
Possibility distribution
Possibility measures
Rough set
Rough sets
Roughness
Set theory
Uncertainty
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Summary:Rough sets theory (RS) is a new tool for processing fuzzy and uncertain knowledge, and has already been applied to many areas successfully. In this paper, by analyzing the basic characteristic of rough set, for the deficiency that the existing rough set model can't effectively solve data reduction and knowledge discovery with possibility feature, we propose rough sets model based on possibility measure (denoted by BP-RS for short), then we analyze the effectiveness of model through an example. The result indicates BP-RS not only have the advantages of classical rough set, but also it can solve effectively information processing problem with the possibility characteristics, and can be widely used in many field such as data mining, evidence theory, artificial intelligence and so on.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212645