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Therefore, it is meaningful to combine matroids with rough sets to solve the optimization problems.
To address classification problems with rough sets, three-way decisions with probabilistic rough set (Yao 2010) are introduced.
The main problem found in query refinements with rough sets is to determine a set of concepts which satisfy a restriction (lower approximation) or a relaxation (upper approximation) of a concept.
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To process such attributes with rough set theory, composite rough set model and corresponding matrix methods were introduced in our previous study.
The results are presented in the form of confusion matrix which shows the classification capability of wavelet features with rough set and fuzzy logic for fault diagnosis of monoblock centrifugal pump.
An architecture of malware analysis realized with probabilistic rough sets based three-way decisions is proposed.
Based on this observation, we combine these methodologies by incorporating Gaussian kernel with fuzzy rough sets and propose a Gaussian kernel approximation based fuzzy rough set model.
Thus, to solve the problems presented by uncertain data analysis and group decision-making in steam turbine fault diagnosis, we propose a new rough set model that combines interval-valued hesitant fuzzy sets with multigranulation rough sets over two universes, called an interval-valued hesitant fuzzy multigranulation rough set over two universes.
Three-way decisions with decision-theoretic rough sets (DTRSs) provide a new methodology to confront risk decision problems.
In this way, three-way decisions with decision-theoretic rough sets are extended to the qualitative environment.
In this paper, numerical algorithms of attribute reduction with neighborhood-covering rough sets are developed by using evidence theory.
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