Improving similarity assessment with entropy-based local weighting

Authors:
Héctor Núñez;Miquel Sánchez-Marré;Ulises Cortés
Affiliations:
Knowledge Engineering & Machine Learning group, Technical University of Catalonia, Barcelona, Catalonia, EU;Knowledge Engineering & Machine Learning group, Technical University of Catalonia, Barcelona, Catalonia, EU;Knowledge Engineering & Machine Learning group, Technical University of Catalonia, Barcelona, Catalonia, EU
Venue:
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Year:
2003

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Abstract

This paper enhances and analyses the power of local weighted similarity measures. The paper proposes a new entropy-based local weighting algorithm (EBL) to be used in similarity assessment to improve the performance of the CBR retrieval task. We describe a comparative analysis of the performance of unweighted similarity measures, global weighted similarity measures, and local weighting similarity measures. The testing has been done using several similarity measures, and some data sets from the UCI Machine Learning Data-base Repository and other environmental databases. Main result is that using EBL, and a weight sensitive similarity measure could improve similarity assessment in case retrieval.