Comparison of the probabilistic approximate classification and the fuzzy set model
Fuzzy Sets and Systems
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
Advances in the Dempster-Shafer theory of evidence
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Variable Consistency Model of Dominance-Based Rough Sets Approach
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Stochastic dominance-based rough set model for ordinal classification
Information Sciences: an International Journal
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
Statistical Model for Rough Set Approach to Multicriteria Classification
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Statistical matching of multiple sources: A look through coherence
International Journal of Approximate Reasoning
Developing a business failure prediction model via RST, GRA and CBR
Expert Systems with Applications: An International Journal
Monotonic Variable Consistency Rough Set Approaches
International Journal of Approximate Reasoning
Conditional probability and fuzzy information
Computational Statistics & Data Analysis
Fuzzy rough sets and multiple-premise gradual decision rules
International Journal of Approximate Reasoning
Optimization heuristics for determining internal rating grading scales
Computational Statistics & Data Analysis
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Rough membership and bayesian confirmation measures for parameterized rough sets
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Information combination operators for data fusion: a comparative review with classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Editorial: Special issue on fuzzy sets in statistics
Computational Statistics & Data Analysis
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Credit scoring analysis is an important activity, especially nowadays after a huge number of defaults has been one of the main causes of the financial crisis. Among the many different tools used to model credit risk, the recent development of rough set models has proved effective. The original development of rough set theory has been widely generalized and combined with other approaches to uncertain reasoning, especially probability and fuzzy set theories. Since coherent conditional probability assessments cope well with the problem of unifying these different approaches, a merging of fuzzy rough set theory with this subjectivist approach is proposed. Specifically, expert partial probabilistic evaluations are encompassed inside a gradual decision rule structure, with coherence of the conclusion as a guideline. In line with Bayesian rough set models, credibility degrees of multiple premises are introduced through conditional probability assessments. Nonetheless, discernibility with this method remains too fine. Therefore, the basic partition is coarsened by equivalence classes based on the arity of positively, negatively and neutrally related criteria. A membership function, which grades the likelihood of default, is introduced by a peculiar choice of t-norms and t-conorms. To build and test the model, real data related to a sample of firms are used.