Several methods for feature selection and weighting have been implemented and tested within the similarity-based framework of classification methods. Features are excluded and ranked according to their contribution to the classification accuracy in the crossvalidation tests. Weighting factors used to compute distances are optimized using global minimization procedures or search-based methods. Our experiments show that, for some datasets, these methods give much better results than classical nearest neighbor methods.

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Projects on similar subject and BACK to the on-line publications of W. Duch.