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Abstract. Framework for Similarity-Based Methods (SBMs) allows to create many algorithms that differ in important aspects. Although no single learning algorithm may outperform other algorithms on all data an almost optimal algorithm may be found within the SBM framework. To avoid tedious experimentation a meta-learning search procedure in the space of all possible algorithms is used to build new algorithms. Each new algorithm is generated by applying admissible extensions to the existing algorithms and the most promising are retained and extended further. Training is performed using parameter optimization techniques. Preliminary tests of this approach are very encouraging.

Abstract. Feature selection is an essential component in all data mining applications. Ranking of futures was made by several inexpensive methods based on information theory. Accuracy of neural, similarity based and decision tree classifiers calculated with reduced number of features. Comparison with computationally more expensive feature elimination methods was made.

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Abstract.The class of similarity based methods (SBM)