Using entropy-based local weighting to improve similarity assessment
This paper enhances and analyses the power of local weighted similarity measures. The paper proposes a new entropy-based local weighting algorithm to be used in similarity assessment to improve the performance of the CBR retrieval task. It has been carried out a comparative analysis of the performan...
| Authors: | , , , , , |
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| Format: | report |
| Publication Date: | 2002 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/97483 |
| Online Access: | https://hdl.handle.net/2117/97483 |
| Access Level: | Open access |
| Keyword: | Entropy Similarity CBR UCI machine learning database repository Local weighted similarity measures Àrees temàtiques de la UPC::Informàtica |
| Summary: | This paper enhances and analyses the power of local weighted similarity measures. The paper proposes a new entropy-based local weighting algorithm to be used in similarity assessment to improve the performance of the CBR retrieval task. It has been carried out 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 Database Repository and other environmental databases. |
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