Fuzzy controls under various fuzzy reasoning methods
Information Sciences: an International Journal - Application of Fuzzy Set Theory
Rough sets: probabilistic versus deterministic approach
International Journal of Man-Machine Studies
A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
Fuzzy implication operators and generalized fuzzy method of cases
Fuzzy Sets and Systems
Variable precision rough set model
Journal of Computer and System Sciences
&agr;-RST: a generalization of rough set theory
Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
An Investigation of beta-Reduct Selection within the Variable Precision Rough Sets Model
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Investigating the Choice of l and u Values in the Extended Variable Precision Rough Sets Model
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Constructive and axiomatic approaches of fuzzy approximation operators
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
A comparison of fuzzy strategies for corporate acquisition analysis
Fuzzy Sets and Systems
Parameterized rough set model using rough membership and Bayesian confirmation measures
International Journal of Approximate Reasoning
Probabilistic rough set approximations
International Journal of Approximate Reasoning
Probabilistic approach to rough sets
International Journal of Approximate Reasoning
Fuzzy rough approximations of process data
International Journal of Approximate Reasoning
Expert Systems with Applications: An International Journal
The model of fuzzy variable precision rough sets
IEEE Transactions on Fuzzy Systems
The investigation of the Bayesian rough set model
International Journal of Approximate Reasoning
Fuzzy-Rough Sets Assisted Attribute Selection
IEEE Transactions on Fuzzy Systems
Variable Precision Extension Of Rough Sets
Fundamenta Informaticae
Comparative study of variable precision rough set model and graded rough set model
International Journal of Approximate Reasoning
Dominance-based rough set model in intuitionistic fuzzy information systems
Knowledge-Based Systems
WSEAS Transactions on Information Science and Applications
Generalization of Pawlak's rough approximation spaces by using δβ-open sets
International Journal of Approximate Reasoning
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Fuzzy probabilistic rough set model on two universes and its applications
International Journal of Approximate Reasoning
An automatic method to determine the number of clusters using decision-theoretic rough set
International Journal of Approximate Reasoning
Information Sciences: an International Journal
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In the past, the choices of @b values to be applied to find the @b-reducts in VPRS for an information system are somewhat arbitrary. In this study, a systematic method which bridges the fuzzy set methodology and probabilistic approach of RS to solve the threshold value @b determination problem in variable precision rough sets (VPRS) is proposed. Different from the existing probabilistic methods, the proposed method relies on the fuzzy membership degrees of each attribute of the objects to calculate @b. The proposed method gives the membership degrees and fuzzy aggregation operators the probabilistic interpretations. Based on the probabilistic interpretations, the threshold value @b of VPRS is directly derived from fuzzy membership degree by Implication Relations and Fuzzy Algorithms, in which the membership degrees are obtained by the standard Fuzzy C-means method. The argument is that errors of system classification would occur in the fuzzy-clustering phase prior to information classification, therefore the threshold value @b should be constrained by the probability of belongingness of an object to the fuzzy clusters, i.e., through the values of membership functions. A few examples are given in the paper to demonstrate the differences with other @b-determining methods.