Robust rank correlation coefficients on the basis of fuzzy
The goal of this paper is to demonstrate that established rank correlation measures are not ideally suited for measuring rank correlation for numerical data that are perturbed by noise. We propose to use robust rank correlation measures based on fuzzy orderings. We demonstrate that the new measures...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2008 |
| 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:2099/13196 |
| Acceso en línea: | https://hdl.handle.net/2099/13196 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial intelligence Intel•ligència artificial Classificació AMS::68 Computer science::68T Artificial intelligence Àrees temàtiques de la UPC::Informàtica::Informàtica teórica |
| Sumario: | The goal of this paper is to demonstrate that established rank correlation measures are not ideally suited for measuring rank correlation for numerical data that are perturbed by noise. We propose to use robust rank correlation measures based on fuzzy orderings. We demonstrate that the new measures overcome the robustness problems of existing rank correlation coe cients. As a rst step, this is accomplished by illustrative examples. The paper closes with an outlook on future research and applications |
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