Optimizing the k-NN metric weights using differential evolution

Publication Type:
Conference Proceeding
Citation:
MCIT'2010: International Conference on Multimedia Computing and Information Technology, 2010, pp. 89 - 92
Issue Date:
2010-06-01
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Traditional k-NN classifier poses many limitations including that it does not take into account each class distribution, importance of each feature, contribution of each neighbor, and the number ofinstances for each class. A Differential evolution (DE) optimization technique is utilized to enhance the performance of k-NN through optimizing the metric weights of features, neighbors and classes. Several datasets are used to evaluate the performance of the proposed DE based metrics and to compare it to some k-NN variants from the literature. Practical experiments indicate that in most cases, incorporating DE in k-NN classification can provide more accurate performance. ©2010 IEEE.
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