A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Floating search methods in feature selection
Pattern Recognition Letters
An introduction to genetic algorithms
An introduction to genetic algorithms
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Further Research on Feature Selection and Classification Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A Framework for Classifier Fusion: Is It Still Needed?
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Advances in Statistical Feature Selection
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
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In this paper we compare recently developed and highly effective sequential feature selection algorithms with approaches based on evolutionary algorithms enabling parallel feature subset selection. We introduce the oscillating search method, employ permutation encoding offering some advantages over the more traditional bitmap encoding for the evolutionary search, and compare these algorithms to the often studied and well-performing sequential forward floating search. For the empirical analysis of these algorithms we utilize three well-known benchmark problems, and assess the quality of feature subsets by means of the statistical Bhattacharyya distance measure.