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Improving similarity assessment with entropy-based local weighting

Author
Nuñez, H.; Sànchez-Marrè, M.; Cortes, U.
Type of activity
Journal article
Journal
Lecture notes in computer science
Date of publication
2003-06
Volume
2689
First page
377
Last page
391
DOI
https://doi.org/10.1007/3-540-45006-8_30 Open in new window
URL
http://link.springer.com/chapter/10.1007/3-540-45006-8_30 Open in new window
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 U...
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group

Participants