Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Integrating robust clustering techniques in S-PLUS
Computational Statistics & Data Analysis
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary model selection in unsupervised learning
Intelligent Data Analysis
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
MaZda-A software package for image texture analysis
Computer Methods and Programs in Biomedicine
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The research presented in this paper aimed at development of a robust feature space exploration technique for unsupervised selection of its subspace for feature vectors classification. Experiments with synthetic and textured image data sets show that current sequential and evolutionary strategies are inefficient in the cases of large feature vector dimensions (reaching the order of 102) and multiple-class problems. Thus, the proposed approach utilizes the concept of hybrid genetic algorithm and adopts it for specific requirements of unsupervised learning.