Paper Info
Title
RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal Neighbourhood
Abstract
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.
Year
DOI
Venue
2002
10.1007/3-540-36755-1_10
ECML
Keywords
Field
DocType
well-known method,whole support,classifier combining rule induction,optimal neighbourhood,rule-based algorithm result,classification accuracy,empirical study,widely-used empirical approach,instance-based learning,automated selection,k-nn method,small neighbourhood,test case,instance based learning,rule based
Data mining,Pattern recognition,Computer science,Expert system,Neighbourhood (mathematics),Rule induction,Artificial intelligence,Acceleration,Knowledge base,Classifier (linguistics),Rule of inference,Empirical research
Conference
Volume
ISSN
ISBN
2430
0302-9743
3-540-44036-4
Citations 
PageRank 
References 
18
1.00
8
Authors
2
Name
Order
Citations
PageRank
Grzegorz Góra1644.38
Arkadiusz Wojna218312.82