Abstract
The article describes a method combining two widely-used empirical approaches: rule induction and instance-based learning. In our algorithm (RIONA) decision is predicted not on the basis of the whole support set of all rules matching a test case, but the support set restricted to a neighbourhood of a test case. The size of the optimal neighbourhood is automatically induced during the learning phase. The empirical study shows the interesting fact that it is enough to consider a small neighbourhood to preserve classification accuracy. The combination of k-NN and a rule-based algorithm results in a significant acceleration of the algorithm using all minimal rules. We study the significance of different components of the presented method and compare its accuracy to well-known methods.
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Góra, G., Wojna, A. (2002). RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal Neighbourhood. In: Elomaa, T., Mannila, H., Toivonen, H. (eds) Machine Learning: ECML 2002. ECML 2002. Lecture Notes in Computer Science(), vol 2430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36755-1_10
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DOI: https://doi.org/10.1007/3-540-36755-1_10
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