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.
CITATION STYLE
Góra, G., & Wojna, A. (2002). RIONA: A classifier combining rule induction and k-NN method with automated selection of optimal neighbourhood. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2430, pp. 111–123). Springer Verlag. https://doi.org/10.1007/3-540-36755-1_10
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