A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Floating search methods in feature selection
Pattern Recognition Letters
Divergence Based Feature Selection for Multimodal Class Densities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic selection and neural modeling for designing pattern classifiers
Genetic selection and neural modeling for designing pattern classifiers
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Feature Selection using Fuzzy Support Vector Machines
Fuzzy Optimization and Decision Making
A Thermodynamical Search Algorithm for Feature Subset Selection
Neural Information Processing
Time space tradeoffs in GA based feature selection for workload characterization
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
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The performance and speed of three classifier-specific feature selection algorithms, the sequential forward (backward) floating search (SFFS (SBFS)) algorithm, the ASFFS (ASBFS) algorithm (its adaptive version), and the genetic algorithm (GA) for large-scale problems are compared. The experimental results showed that 1) ASFFS (ASBFS) has better performance than does SFFS (SBFS) but requires much computation time, 2) much training in GA with a larger number of generations or with a larger population size, or both, is effective, 3) the performance of SFFS (SBFS) is comparable to that of GA with less training, and the performance of ASFFS (ASBFS) is comparable to that of GA with much training, but in terms of speed GA is better than ASFFS (ASBFS) for large-scale problems.