Computer
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
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Data Mining and Knowledge Discovery
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
IEEE Transactions on Computers
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Proceedings of the ACM International Conference on Image and Video Retrieval
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The goal of this paper is to study if there is a dependency between a selected feature vector at each generation of the genetic algorithm and the resulting fitness. In order to see the relation between these parameters, we first use Discrete Cosine Transforms (DCT) to transform each image as a feature vector (i.e., Frequency Feature Subset (FFS)). A Genetic Algorithm (GA) is then used to select a subset of features from the low-dimensional representation by removing certain DCT coefficients that do not seem to encode important information about recognition task. When using SVM, two problems are confronted: how to choose the optimal input feature subset for SVM, and how to set the best kernel parameters. Therefore, obtaining the optimal feature subset and SVM parameters must occur simultaneously. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem.