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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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Tom
Strony
369--390
Opis fizyczny
tab., wykr., bibliogr. 31 poz.
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autor
autor
- Institute of Informatics Warsaw University ul. Banacha 2 02-097 Warsaw, Poland, ggora@mimuw.edu.pl
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Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0004-0039