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Escola Superior de Tecnologia e Gestão (ESTG) >
Artigos em Revista Internacional (ESTG) >
Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10314/3247
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| Título: | On the Impact of Distance Metrics in Instance-Based Learning Algorithms |
| Autores: | Lopes, Noel
Ribeiro, Bernardete |
| Palavras Chave: | Distance metrics
Instance-based learning
Incremental learning
Nearest Neighbor
Incremental Hypersphere Classifier (IHC) |
| Data: | 2015 |
| Editora: | Springer International Publishing Switzerland |
| Resumo: | In this paper we analyze the impact of distinct distance metrics in instance-based learning algorithms. In particular, we look at the well-known 1-Nearest Neighbor (NN) algorithm and the Incremental Hypersphere Classifier (IHC) algorithm, which proved to be efficient in large-scale recognition problems and online learning. We provide a detailed empirical evaluation on fifteen datasets with several sizes and dimensionality. We then statistically show that the Euclidean and Manhattan metrics significantly yield good results in a wide range of problems.
However, grid-search like methods are often desirable to determine the best matching metric depending on the problem and algorithm. |
| URI: | http://hdl.handle.net/10314/3247 |
| Aparece nas Colecções: | Artigos em Revista Internacional (ESTG)
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Ficheiros deste Registo:
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Descrição |
Tamanho | Formato |
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| Revista_Noel1.pdf | | 514Kb | Adobe PDF | Ver/Abrir | |
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