Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nearest neighbor classifier: simultaneous editing and feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm
Pattern Recognition Letters
Efficient huge-scale feature selection with speciated genetic algorithm
Pattern Recognition Letters
Automatic video segmentation using genetic algorithms
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
A genetic feature weighting scheme for pattern recognition
Integrated Computer-Aided Engineering
Design of nearest neighbor classifiers: multi-objective approach
International Journal of Approximate Reasoning
Using a genetic algorithm for editing k-nearest neighbor classifiers
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
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By appropriate editing of the reference set and judicious selection of features, we can obtain an optimal nearest neighbor (NN) classifier that maximizes the accuracy of classification and saves computational time and memory resources. In this paper, we propose a new method for simultaneous reference set editing and feature selection for a nearest neighbor classifier. The proposed method is based on the genetic algorithm and employs different genetic encoding strategies according to the size of the problem, such that it can be applied to classification problems of various scales. Compared with the conventional methods, the classifier uses some of the considered references and features, not all of them, but demonstrates better classification performance. To demonstrate the performance of the proposed method, we perform experiments on various databases.