Justification-Based Selection of Training Examples for Case Base Reduction
Abstract
Maintaining compact and competent case bases has become a main topic of Case Based Reasoning (CBR) research. The main goal is to obtain a compact case base (with a reduced number of cases) without losing accuracy. In this work we present JUST , a technique to reduce the size of a case base while maintaining the classification accuracy of the CBR system. JUST uses justifications in order to select a subset of cases from the original case base that will form the new reduced case base. A justification is an explanation that the CBR system generates to justify the solution found for a given problem. Moreover, we present empirical evaluation in various data sets showing that JUST is an effective case base reduction technique that maintains the classification accuracy of the case base.
Cite
Text
Ontañón and Plaza. "Justification-Based Selection of Training Examples for Case Base Reduction." European Conference on Machine Learning, 2004. doi:10.1007/978-3-540-30115-8_30Markdown
[Ontañón and Plaza. "Justification-Based Selection of Training Examples for Case Base Reduction." European Conference on Machine Learning, 2004.](https://mlanthology.org/ecmlpkdd/2004/ontanon2004ecml-justificationbased/) doi:10.1007/978-3-540-30115-8_30BibTeX
@inproceedings{ontanon2004ecml-justificationbased,
title = {{Justification-Based Selection of Training Examples for Case Base Reduction}},
author = {Ontañón, Santiago and Plaza, Enric},
booktitle = {European Conference on Machine Learning},
year = {2004},
pages = {310-321},
doi = {10.1007/978-3-540-30115-8_30},
url = {https://mlanthology.org/ecmlpkdd/2004/ontanon2004ecml-justificationbased/}
}