C4.5: programs for machine learning
C4.5: programs for machine learning
Nearest neighbor classifier: simultaneous editing and feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm
Pattern Recognition Letters
Granulation and nearest neighborhoods: rough set approach
Granular computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Prototype induction and attribute selection via evolutionary algorithms
Intelligent Data Analysis
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
Intelligent evolutionary algorithms for large parameter optimization problems
IEEE Transactions on Evolutionary Computation
A memetic algorithm for evolutionary prototype selection: A scaling up approach
Pattern Recognition
A divide-and-conquer recursive approach for scaling up instance selection algorithms
Data Mining and Knowledge Discovery
Constructing ensembles of classifiers by means of weighted instance selection
IEEE Transactions on Neural Networks
An efficient design of a nearest neighbor classifier for various-scale problems
Pattern Recognition Letters
Applying electromagnetism-like mechanism for feature selection
Information Sciences: an International Journal
A scalable approach to simultaneous evolutionary instance and feature selection
Information Sciences: an International Journal
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The goal of designing optimal nearest neighbor classifiers is to maximize classification accuracy while minimizing the sizes of both reference and feature sets. A usual way is to adaptively weight the three objectives as an objective function and then use a single-objective optimization method for achieving this goal. This paper proposes a multi-objective approach to cope with the weight tuning problem for practitioners. A novel intelligent multi-objective evolutionary algorithm IMOEA is utilized to simultaneously edit compact reference and feature sets for nearest neighbor classification. Three comparison studies are designed to evaluate performance of the proposed approach. It is shown empirically that the IMOEA-designed classifiers have high classification accuracy and small sizes of reference and feature sets. Moreover, IMOEA can provide a set of good solutions for practitioners to choose from in a single run. The simulation results indicate that the IMOEA-based approach is an expedient method to design nearest neighbor classifiers, compared with an existing single-objective approach.