RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning

• Autorzy: Góra G. , Wojna A.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

machine learning; instance-based learning; rule induction; nearest neighbour method

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2002
• Tom: Vol. 51, nr 4
• Strony: 369--390
• Opis fizyczny: tab., wykr., bibliogr. 31 poz.

Twórcy

autor

Góra G.

autor

Wojna A.

• Institute of Informatics Warsaw University ul. Banacha 2 02-097 Warsaw, Poland,