Proceedings

EnergyCompress: A General Case Base Learning Strategy

Fadi Badra, Esteban Marquer, Marie-Jeanne Lesot, Miguel Couceiro, David Leake

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 4339-4346.

https://doi.org/10.24963/ijcai.2025/483

Keywords

  • Knowledge Representation and Reasoning: KRR: Case-based reasoning

Abstract

Case-based prediction (CBP) methods do not learn a model of the target decision function but instead perform an inference process that depends on two similarity measures and a reference case base. This paper proposes a strategy, called EnergyCompress, to learn an effective case base by selecting relevant cases from an initial set. Use of EnergyCompress decreases CBP inference time, through case base compression, and also increases prediction performance, for a wide variety of CBP algorithms. EnergyCompress relies on the proposition of a general formulation of the CBP task in the framework of energy-based models, which leads to a new and valuable characterization of the notion of competence in case-based reasoning, in particular at the source case level. Extensive experimental results on 18 benchmark datasets comparing EnergyCompress to 5 reference algorithms for case base maintenance support the benefit of the proposed strategy.

Fadi Badra, Esteban Marquer, Marie-Jeanne Lesot, Miguel Couceiro, and David Leake. 2025. EnergyCompress: A General Case Base Learning Strategy. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 4339–4346. https://doi.org/10.24963/ijcai.2025/483
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