RIONA: A new classification system combining rule induction and instance-based learning. The article describes a method combining two widely-used empirical approaches to learning from examples: 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 achieve classification accuracy comparable to an algorithm considering the whole learning set. The combination of (k)-NN and a rule-based algorithm results in a significant acceleration of the algorithm using all minimal rules. Moreover, the presented classifier has high accuracy for both kinds of domains: more suitable for (k)-NN classifiers and more suitable for rule based classifiers.
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References in zbMATH (referenced in 9 articles , 2 standard articles )
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- Góra, Grzegorz; Wojna, Arkadiusz: RIONA: A classifier combining rule induction and k-NN method with automated selection of optimal neighbourhood (2002)