Logical analysis of binary data with missing bits
Artificial Intelligence
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Branch-and-Bound Algorithms for the Test Cover Problem
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
A MINSAT Approach for Learning in Logic Domains
INFORMS Journal on Computing
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Design of Logic-based Intelligent Systems
Design of Logic-based Intelligent Systems
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining and genetic algorithm based gene/SNP selection
Artificial Intelligence in Medicine
A probabilistic heuristic for a computationally difficult set covering problem
Operations Research Letters
Expert Systems with Applications: An International Journal
Logic based methods for SNPs tagging and reconstruction
Computers and Operations Research
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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In this paper we investigate logic classification and related feature selection algorithms for large biomedical data sets. When the data is in binary/logic form, the feature selection problem can be formulated as a Set Covering problem of very large dimensions, whose solution is computationally challenging. We propose an alternative approximated formulation for feature selection that results in an extension of Set Covering of compact size, and use the logic classifier Lsquare to test its performances on two well-known data sets. An ad hoc metaheuristic of the GRASP type is used to solve efficiently the feature selection problem. A simple and effective method to convert rational data into logic data by interval mapping is also described. The computational results obtained are promising and the use of logic models, that can be easily understood and integrated with other domain knowledge, is one of the major strengths of this approach.