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.
Cite
Text
Góra and Wojna. "RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal Neighbourhood." European Conference on Machine Learning, 2002. doi:10.1007/3-540-36755-1_10Markdown
[Góra and Wojna. "RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal Neighbourhood." European Conference on Machine Learning, 2002.](https://mlanthology.org/ecmlpkdd/2002/gora2002ecml-riona/) doi:10.1007/3-540-36755-1_10BibTeX
@inproceedings{gora2002ecml-riona,
title = {{RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal Neighbourhood}},
author = {Góra, Grzegorz and Wojna, Arkadiusz},
booktitle = {European Conference on Machine Learning},
year = {2002},
pages = {111-123},
doi = {10.1007/3-540-36755-1_10},
url = {https://mlanthology.org/ecmlpkdd/2002/gora2002ecml-riona/}
}