Search and global minimization in similarity-based methods
Włodzisław Duch, Karol Grudziński · 2003
The class of similarity based methods (SBM) covers most neural models and many other classifiers. Performance of such methods is significantly improved if irrelevant features are removed and feature weights introduced, scaling their influence on calculation of similarity. Several methods for feature selection and weighting are described. As an alternative to the global minimization procedures computationally efficient best-first search methods are advocated. Although these methods can be used with any SBM classifier they have been tested using the k-NN method since it is relatively fast and for some databases gives excellent results. A few illustrative examples show significant improvements due to the feature weighting and selection.