Towards a characterisation of the behaviour of stochastic local search algorithms for SAT
Artificial Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A machine program for theorem-proving
Communications of the ACM
Solution Techniques for Constraint Satisfaction Problems: Foundations
Artificial Intelligence Review
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Reduction algorithms based on discernibility matrix: the ordered attributes method
Journal of Computer Science and Technology
Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF
Applied Intelligence
Dynamic Flexible Constraint Satisfaction
Applied Intelligence
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Boolean Reasoning for Feature Extraction Problems
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
The Quest for Efficient Boolean Satisfiability Solvers
CADE-18 Proceedings of the 18th International Conference on Automated Deduction
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
IP algorithms in compact rough classification modeling
Intelligent Data Analysis
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Discernibility matrix simplification for constructing attribute reducts
Information Sciences: an International Journal
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
A rough set approach to feature selection based on ant colony optimization
Pattern Recognition Letters
Computationally efficient sup-t transitive closure for sparse fuzzy binary relations
Fuzzy Sets and Systems
Are more features better? a response to attributes reduction using fuzzy rough sets
IEEE Transactions on Fuzzy Systems
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
Two novel feature selection methods based on decomposition and composition
Expert Systems with Applications: An International Journal
Fuzzy Rough Sets: The Forgotten Step
IEEE Transactions on Fuzzy Systems
Unsupervised fuzzy-rough set-based dimensionality reduction
Information Sciences: an International Journal
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Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. In particular, solution to this has found successful application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would otherwise be impossible to process further. Recent examples include text processing and web content classification. Rough set theory has been used as such a dataset pre-processor with much success, but current methods are inadequate at finding globally minimal reductions, the smallest sets of features possible. This paper proposes a technique that considers this problem from a propositional satisfiability perspective. In this framework, globally minimal subsets can be located and verified.