Algorithms for classification based on k-NN
In this paper we focus on methods that solve classification tasks based on distances, and we introduce some variants of the basic k-NN method adding up to 3 characteristics. The experiments reveal a relationship between the accuracy of 1-NN (distances) and the accuracy of the methods based on those...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2007 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/10929 |
| Acceso en línea: | https://hdl.handle.net/2099/10929 |
| Access Level: | acceso abierto |
| Palabra clave: | Algorithms Algorismes Classificació AMS::68 Computer science::68W Algorithms Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica::Algorísmica i teoria de la complexitat |
| Sumario: | In this paper we focus on methods that solve classification tasks based on distances, and we introduce some variants of the basic k-NN method adding up to 3 characteristics. The experiments reveal a relationship between the accuracy of 1-NN (distances) and the accuracy of the methods based on those distances. We propose a heuristics according to this observation and test its correctness. We study the usefulness of the proposed methods epsilon-ball, epsilon-ball^{k-NN} and epsilon-ball^{1-NN}, and make an exhaustive comparison using six different distance functions and 68 data sets, including UCI--Repository and artificial data sets. The proposed methods are useful and significantly outperform k-NN frequently. We have also found some evidence about the weakness of k-NN when the optimal value of $k$ varies in different regions along the space. |
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