A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Editing for the k-nearest neighbors rule by a genetic algorithm
Pattern Recognition Letters - Special issue on genetic algorithms
Further Research on Feature Selection and Classification Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
PLEASE: A Prototype Learning System Using Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Selecting Features and Objects for Mixed and Incomplete Data
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Information Sciences: an International Journal
Simultaneous features and objects selection for mixed and incomplete data
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Incorporating knowledge in evolutionary prototype selection
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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This paper proposes a genetic-algorithm-based approach for finding a compact reference set used in nearest neighbor classification. The reference set is designed by selecting a small number of reference patterns from a large number of training patterns using a genetic algorithm. The genetic algorithm also removes unnecessary features. The reference set in our nearest neighbor classification consists of selected patterns with selected features. A binary string is used for representing the inclusion (or exclusion) of each pattern and feature in the reference set. Our goal is to minimize the number of selected patterns, to minimize the number of selected features, and to maximize the classification performance of the reference set. The effectiveness of our approach is examined by computer simulations on commonly used data sets.