Integrating Instance Selection, Instance Weighting and Feature Weighting for Nearest Neighbor Classifiers by Co-evolutionary Algorithms - Complementary Material
This Website contains complementary material to the paper:
J. Derrac, I.Triguero, S. García and F.Herrera, Integrating Instance Selection, Instance Weighting and Feature Weighting for Nearest Neighbor Classifiers by Co-evolutionary Algorithms. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42:5 (2012) 1383-1397, doi: 10.1109/TSMCB.2012.2191953 
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Abstract
J. Derrac, I.Triguero, S. García and F.Herrera, Integrating Instance Selection, Instance Weighting and Feature Weighting for Nearest Neighbor Classifiers by Co-evolutionary Algorithms.
Cooperative Co-evolution is a successful trend of Evolutionary Computation which allows us to define partitions of the domain of a given problem, or to integrate several related techniques into one, by the use of Evolutionary Algorithms. It is possible to apply it to the development of advanced classification methods, which integrate several machine learning techniques into a single proposal. A novel approach integrating Instance Selection, Instance Weighting and Feature Weighting into the framework of a co-evolutionary model, is presented in this paper. We compare it with a wide range of evolutionary and non-evolutionary related methods, in order to show the benefits of the employment of co-evolution to apply the techniques considered simultaneously. The results obtained, contrasted through nonparametric statistical tests, show that our proposal outperforms other methods in the comparison, thus becoming a suitable tool in the task of enhancing the Nearest Neighbor classifier.