A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Floating search methods in feature selection
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Multilevel hypergraph partitioning: applications in VLSI domain
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tabu Search
An accelerated procedure for recursive feature ranking on microarray data
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
An introduction to variable and feature selection
The Journal of Machine Learning Research
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamics of Local Search Trajectory in Traveling Salesman Problem
Journal of Heuristics
A first multilevel cooperative algorithm for capacitated multicommodity network design
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
An empirical analysis of search in GSAT
Journal of Artificial Intelligence Research
Explicit and Emergent Cooperation Schemes for Search Algorithms
Learning and Intelligent Optimization
Tournament searching method to feature selection problem
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
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The automated analysis of patients' biomedical data can be used to derive diagnostic and prognostic inferences about the observed patients. Many noninvasive techniques for acquiring biomedical samples generate data that are characterized by a large number of distinct attributes (i.e., features) and a small number of observed patients (i.e., samples). Using these biomedical data to derive reliable inferences, such as classifying a given patient as either cancerous or noncancerous, requires that the ratio r of the number of samples to the number of features be within the range 5