Learning similarity metrics from case solution similarity

Authors:
Carlos Morell;Rafael Bello;Ricardo Grau;Yanet Rodríguez
Affiliations:
Computer Sciences Department., Universidad Central de Las Villas, Cuba;Computer Sciences Department., Universidad Central de Las Villas, Cuba;Computer Sciences Department., Universidad Central de Las Villas, Cuba;Computer Sciences Department., Universidad Central de Las Villas, Cuba
Venue:
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Year:
2006

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Abstract

Defining similarity metrics is one of the most important tasks when developing Case Based Reasoning (CBR) systems. The performance of the system heavily depends on the correct definition of its similarity metric. To reduce this sensitivity, similarity functions are parameterized with weights for features. Most approaches to learning feature weights assume CBR systems for classification tasks. In this paper we propose the use of similarity between case solutions as a heuristic to estimate similarity between case descriptions. This estimation is used to adjust weights for features. We present an experiment in the domain of Case Based Process Planning that shows the effectiveness of this approach.