Paper Info
Title
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
Abstract
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.
Year
Venue
Keywords
2002
Fundam. Inform.
k-nn classifier,rule-based algorithm result,optimal neighbourhood,classification accuracy,nearest neighbour method,empirical study,high accuracy,instance-based learning,whole learning set,machine learning,rule induction,small neighbourhood,new classification system combining,test case,rule based,instance based learning,classification system
Field
DocType
Volume
Stability (learning theory),Instance-based learning,Semi-supervised learning,Active learning (machine learning),Artificial intelligence,Rule induction,Linear classifier,Probabilistic classification,Mathematics,Machine learning,Learning classifier system
Journal
51
Issue
ISSN
Citations 
4
0169-2968
37
PageRank 
References 
Authors
1.93
20
2
Name
Order
Citations
PageRank
Grzegorz Góra1644.38
Arkadiusz Wojna218312.82