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Ensemble selector for attribute reduction
Applied Soft Computing, 2018Abstract Through abstracting commonness from the existing heuristic algorithms, control strategies bring us higher level understandings of building reducts in rough set theory. To further improve the performances and strengthen the applicabilities of the addition control strategy, an ensemble selector is introduced into such framework.
Xibei Yang, Yiyu Yao
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Fuzzy Rough Attribute Reduction for Categorical Data
IEEE transactions on fuzzy systems, 2020Classical rough set theory is considered a useful tool for dealing with the uncertainty of categorical data. The major deficiency of this model is that the classical rough set model is sensitive to noise in classification learning due to the stringent ...
Changzhong Wang +4 more
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Information system attribute reduction
Third International Workshop on Advanced Computational Intelligence, 2010This paper describes the information systems as a family of equivalence relations or functions. It brings forward the notion of separation polynomial, which is an expression found by the separators (complement of the equivalence relations) via finite union operations and intersection operations.
Shaobai Chen, Mengfei Cao, Xiaodan Chen
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IEEE Transactions on Systems, Man & Cybernetics. Systems
Robust knowledge acquisition approaches are one of the research hotspots in data mining. The fuzzy dominance rough sets (FDRS) model is an important knowledge acquisition tool for ordinal classification tasks. However, it has been proved in practice that
Bin-Bin Sang +4 more
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Robust knowledge acquisition approaches are one of the research hotspots in data mining. The fuzzy dominance rough sets (FDRS) model is an important knowledge acquisition tool for ordinal classification tasks. However, it has been proved in practice that
Bin-Bin Sang +4 more
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Fuzzy Rough Attribute Reduction Based on Fuzzy Implication Granularity Information
IEEE transactions on fuzzy systemsFuzzy rough set model is a powerful tool for handling attribute reduction tasks for complex data. While the fuzzy rough set model commonly employs fuzzy information entropy to measure attribute uncertainty, utilizing fuzzy conditional information entropy
Jianhua Dai, Zhilin Zhu, Xiongtao Zou
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Knowledge-Based Systems, 2020
For incomplete data with mixed numerical and symbolic attributes, attribute reduction based on neighborhood multi-granulation rough sets (NMRS) is an important method to improve the classification performance.
Lin Sun +4 more
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For incomplete data with mixed numerical and symbolic attributes, attribute reduction based on neighborhood multi-granulation rough sets (NMRS) is an important method to improve the classification performance.
Lin Sun +4 more
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Minimal Attribute Space Bias for Attribute Reduction
2007Attribute reduction is an important inductive learning issue addressed by the Rough Sets society.Most existing works on this issue use the minimal attribute bias, i.e., searching for reducts with the minimal number of attributes. But this bias does not work well for datasets where different attributes have different sizes of domains.
Fan Min 0001 +3 more
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Universal Attribute Reduction Problem
2007In the paper, some generalizations of the notions of reduct and test (superreduct) are considered. The accuracy of greedy algorithm for construction of partial test is investigated. A lower bound on the minimal cardinality of partial reducts based on an information on greedy algorithm work is studied.
Mikhail Ju. Moshkov +2 more
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A General Definition of an Attribute Reduct
2007A reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of a given information table. A general definition of an attribute reduct is presented. Specifically, we discuss the following issues: First, there are a variety of properties that can be observed in an information table ...
Yan Zhao 0001 +3 more
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Consistency Based Attribute Reduction
2007Rough sets are widely used in feature subset selection and attribute reduction. In most of the existing algorithms, the dependency function is employed to evaluate the quality of a feature subset. The disadvantages of using dependency are discussed in this paper. And the problem of forward greedy search algorithm based on dependency is presented.
Qinghua Hu +3 more
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