Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Floating search methods in feature selection
Pattern Recognition Letters
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
How to solve it: modern heuristics
How to solve it: modern heuristics
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Evolutionary feature selection for classification: a plug-in hybrid vehicle adoption application
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Genetic algorithms with a novel encoding scheme for feature selection are introduced. The proposed genetic algorithm is restricted to a particular predetermined feature subset size where the local optimal set of features is searched for. The encoding scheme limits the length of the individual to the specified subset size, whereby each gene has a value in the range from 1 to the total number of available features. This article also gives a comparative study of suboptimal feature selection methods using real-world data. The validation of the optimized results shows that the true feature subset size is significantly smaller than the global optimum found by the optimization algorithms.