Artificial Intelligence Review - Special issue on lazy learning
Artificial Intelligence Review - Special issue on lazy learning
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)
Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
International Journal of Intelligent Systems
The Top Ten Algorithms in Data Mining
The Top Ten Algorithms in Data Mining
Information Sciences: an International Journal
IPADE: iterative prototype adjustment for nearest neighbor classification
IEEE Transactions on Neural Networks
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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The nearest neighbor rule is one of the most representative methods in data mining. In recent years, a great amount of proposals have arisen for improving its performance. Among them, instance selection is highlighted due to its capabilities for improving the accuracy of the classifier and its efficiency simultaneously, by editing noise and reducing considerably the size of the training set. It is also possible to remark the role of feature and instance weighting as outstanding methodologies for improving further the performance of the nearest neighbor rule. In this work we present a new co-evolutionary algorithm for combining the former techniques. Its performance is compared with evolutionary approaches performing instance selection, instance weighting and feature weighting in isolation, as well as with the nearest neighbor classifier. The results obtained, contrasted through nonparametric statistical tests, supports the capabilities of co-evolution as a outstanding strategy for joining several proposals for enhancing the nearest neighbor rule.