|Títol||Justification-based Selection of Training Examples for Case Base Reduction|
|Publication Type||Conference Paper|
|Year of Publication||2004|
|Authors||Ontañón S, Plaza E|
|Editor||, Pedresh D.|
|Conference Name||Lecture Notes in Artificial Intelligence|
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.
- Quant a IIIA