Comparison of the probabilistic approximate classification and the fuzzy set model
Fuzzy Sets and Systems
A decision-theoretic roguth set model
Methodologies for intelligent systems, 5
Variable precision rough set model
Journal of Computer and System Sciences
Advances in the Dempster-Shafer theory of evidence
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Inclusion degree: a perspetive on measures for rough set data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Variable Precision Rough Sets with Asymmetric Bounds
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
Topological approaches to covering rough sets
Information Sciences: an International Journal
Consistency measure, inclusion degree and fuzzy measure in decision tables
Fuzzy Sets and Systems
Parameterized rough set model using rough membership and Bayesian confirmation measures
International Journal of Approximate Reasoning
Editorial: Probabilistic rough sets: Approximations, decision-makings, and applications
International Journal of Approximate Reasoning
Probabilistic rough set approximations
International Journal of Approximate Reasoning
Probabilistic approach to rough sets
International Journal of Approximate Reasoning
Variable precision rough set for group decision-making: An application
International Journal of Approximate Reasoning
Modeling of High Quality Granules
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Exploring the boundary region of tolerance rough sets for feature selection
Pattern Recognition
Criteria for choosing a rough set model
Computers & Mathematics with Applications
Granulations Based on Semantics of Rough Logical Formulas and Its Reasoning
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Variable-precision dominance-based rough set approach and attribute reduction
International Journal of Approximate Reasoning
A new customized document categorization scheme using rough membership
Applied Soft Computing
Adapted variable precision rough set approach for EEG analysis
Artificial Intelligence in Medicine
The investigation of the Bayesian rough set model
International Journal of Approximate Reasoning
A Distance Measure Approach to Exploring the Rough Set Boundary Region for Attribute Reduction
IEEE Transactions on Knowledge and Data Engineering
A variable precision rough set approach to the remote sensing land use/cover classification
Computers & Geosciences
The superiority of three-way decisions in probabilistic rough set models
Information Sciences: an International Journal
Determination of the threshold value β of variable precision rough set by fuzzy algorithms
International Journal of Approximate Reasoning
Applying variable precision rough set model for clustering student suffering study's anxiety
Expert Systems with Applications: An International Journal
Modeling rough granular computing based on approximation spaces
Information Sciences: an International Journal
Comparative study of variable precision rough set model and graded rough set model
International Journal of Approximate Reasoning
Bayesian rough set model: A further investigation
International Journal of Approximate Reasoning
Probabilistic rough set over two universes and rough entropy
International Journal of Approximate Reasoning
Graded rough set model based on two universes and its properties
Knowledge-Based Systems
Generalization of Pawlak's rough approximation spaces by using δβ-open sets
International Journal of Approximate Reasoning
Fundamenta Informaticae - Advances in Rough Set Theory
Set-based granular computing: A lattice model
International Journal of Approximate Reasoning
A novel cognitive system model and approach to transformation of information granules
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Information Sciences: an International Journal
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The precision and grade of the approximate space are two fundamental quantitative indexes that measure the relative and absolute quantitative information, respectively. The double quantification of the precision and grade is a relatively new subject, and its effective implementation remains an open problem. This paper approaches the double quantification problem using basic rough set models. The Cartesian product is a natural operator for combining the two indexes given their completeness and complementary natures, and we construct two new models using this strategy. The fundamental items (i.e., the complete system, quantitative semantics and optimal computing) of the model regions are studied using granular computing. First, the model regions (MR granules) and basic model regions (BMR granules) are defined in the traditional fashion using logical double-quantitative semantics; basic semantics (BS) is provided for the double-semantic description, and the semantic extraction of the MR and BMR granules is realized within the BS framework. Computing granules (BMRC granules) are then proposed for the basic model regions to optimize the computation, and a two-dimensional plane and granular hierarchical structure are provided. Two basic algorithms for computing the MR and BMR granules are proposed and analyzed, and the BMRC-granules algorithm generally exhibits superior performance in terms of the temporal and spatial complexity. We also explore the properties of the approximation operators and the notions of attribute approximate dependence and reduction. Finally, we provide an example application from the medical field. The two models provide a basic double quantification of the precision and grade and have concrete double-quantitative semantics; they also represent a quantitatively complete expansion of the Pawlak model.