Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Application of neural networks and genetic algorithms in the classification of endothelial cells
Pattern Recognition Letters - special issue on pattern recognition in practice V
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Sets and Systems - Featured Issue: Selected papers from ACIDCA 2000
Efficient huge-scale feature selection with speciated genetic algorithm
Pattern Recognition Letters
Multi-objective Feature Selection with NSGA II
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Feature subset selection by genetic algorithms and estimation of distribution algorithms
Artificial Intelligence in Medicine
Optimal training subset in a support vector regression electric load forecasting model
Applied Soft Computing
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In this paper, we propose a new feature selection method based on a hierarchical genetic algorithm (GA) with a new evaluation function and a bi-coded representation. The hierarchical GA with homogeneous and heterogeneous population is used to minimize the computational load and to accelerate convergence speed. The fitness function is designed to find the solution that both maximizes the recognition rate and minimizes the feature set size. Each solution candidate is represented by two chromosomes which lengths are identical to the number of available features. The first binary chromosome represents the presence of features in the solution candidate; the second represents the confidence rates of features, which are used to assign different weights to features during the classification procedure and to achieve more accurate classifier. The proposed method is tested using five databases and is shown to outperform many commonly used feature selection algorithms.