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C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
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ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
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Many sophisticated classification algorithms have been proposed. However, there is no clear methodology of comparing the results among different methods. According to our experiments on the popular datasets, k-NN with properly tuned parameters performs on average best. Tuning the parametres include the proper k, proper distance measure and proper weighing functions. k-NN has a zero training time and the test time can be significantly reduced by prior reference vector selection, which needs to be done only once or by applying advanced nearest neighbor search strategies (like KDtree algorithm). Thus we propose that instead of comparing new algorithms with an author's choice of old ones (which may be especially selected in favour of his method), the new method would be rather compared first with properly tuned k-NN as a gold standard. And based on the comparison the author of the new method would have to aswer the question: "Do we really need this method since we already have k-NN?"