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ECML
2004
Springer

Justification-Based Selection of Training Examples for Case Base Reduction

9 years 5 months ago
Justification-Based Selection of Training Examples for Case Base Reduction
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.
Santiago Ontañón, Enric Plaza
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2004
Where ECML
Authors Santiago Ontañón, Enric Plaza
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