Towards a characterisation of the behaviour of stochastic local search algorithms for SAT
Artificial Intelligence
A machine program for theorem-proving
Communications of the ACM
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
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
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
IP algorithms in compact rough classification modeling
Intelligent Data Analysis
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
Feature selection with test cost constraint
International Journal of Approximate Reasoning
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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 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 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, minimal subsets can be located and verified. An initial experimental investigation is conducted, comparing the new method with a standard rough set-based feature selector.