@conference {IIIA-2004-926, title = {Justification-based Selection of Training Examples for Case Base Reduction}, booktitle = {Lecture Notes in Artificial Intelligence}, volume = {3201}, number = {15}, year = {2004}, pages = {310-321}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, 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.}, author = {Santiago Onta{\~n}{\'o}n and Enric Plaza}, editor = {Esposito F. , Giannotti, F. and Pedresh , D.} }