A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
An introduction to genetic algorithms
An introduction to genetic algorithms
Pattern recognition and image analysis
Pattern recognition and image analysis
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Neural-network feature selector
IEEE Transactions on Neural Networks
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This paper describes a novel feature selection algorithm which utilizes a genetic algorithm to select a feature subset in conjunction with the weights for a three-layer feedforward network classifier. The algorithm was tested on the "ionosphere" data set from UC Irvine, and on an artifically generated data set. This approach produces results comparable to those reported for other algorithms on the ionosphere data, but using fewer input features and a simpler neural network architecture. These results indicate that tailoring a neural network classifier to a specific subset of features has the potential to produce a classifier with low classification error and good generalizability.