Rough sets: probabilistic versus deterministic approach
International Journal of Man-Machine Studies
A decision-theoretic roguth set model
Methodologies for intelligent systems, 5
A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
Relational interpretations of neighborhood operators and rough set approximation operators
Information Sciences—Informatics and Computer Science: An International Journal
Rules in incomplete information systems
Information Sciences: an International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
On the Extension of Rough Sets under Incomplete Information
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Probabilistic rough set approximations
International Journal of Approximate Reasoning
Probabilistic approach to rough sets
International Journal of Approximate Reasoning
Decision-theoretic rough set models
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
Local and global approximations for incomplete data
Transactions on rough sets VIII
A Local Version of the MLEM2 Algorithm for Rule Induction
Fundamenta Informaticae - Understanding Computers' Intelligence Celebrating the 100th Volume of Fundamenta Informaticae in Honour of Helena Rasiowa
An empirical comparison of rule sets induced by LERS and probabilistic rough classification
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Probabilistic model criteria with decision-theoretic rough sets
Information Sciences: an International Journal
Generalized parameterized approximations
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
An interval set model for learning rules from incomplete information table
International Journal of Approximate Reasoning
Definability and other properties of approximations for generalized indiscernibility relations
Transactions on Rough Sets XI
Probabilistic rough set over two universes and rough entropy
International Journal of Approximate Reasoning
Two Semantic Issues in a Probabilistic Rough Set Model
Fundamenta Informaticae - Advances in Rough Set Theory
Fundamenta Informaticae - Advances in Rough Set Theory
International Journal of Approximate Reasoning
Local probabilistic approximations for incomplete data
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Experiments on rule induction from incomplete data using three probabilistic approximations
GRC '12 Proceedings of the 2012 IEEE International Conference on Granular Computing (GrC-2012)
Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets
International Journal of Approximate Reasoning
An axiomatic characterization of probabilistic rough sets
International Journal of Approximate Reasoning
Feature selection with test cost constraint
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Rule acquisition and complexity reduction in formal decision contexts
International Journal of Approximate Reasoning
An automatic method to determine the number of clusters using decision-theoretic rough set
International Journal of Approximate Reasoning
Feature selection with test cost constraint
International Journal of Approximate Reasoning
Updating attribute reduction in incomplete decision systems with the variation of attribute set
International Journal of Approximate Reasoning
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In this paper we discuss a generalization of the idea of probabilistic approximations. Probabilistic (or parameterized) approximations, studied mostly in variable precision rough set theory, were originally defined using equivalence relations. Recently, probabilistic approximations were defined for arbitrary binary relations. Such approximations have an immediate application to data mining from incomplete data because incomplete data sets are characterized by a characteristic relation which is reflexive but not necessarily symmetric or transitive. In contrast, complete data sets are described by indiscernibility which is an equivalence relation. The main objective of this paper was to compare experimentally, for the first time, two generalizations of probabilistic approximations: global and local. Additionally, we explored the problem how many distinct probabilistic approximations may be defined for a given data set.