Problems Selection Under Dynamic selection of the best base classifier in One versus One: PSEUDOVO
Class binarization techniques are used to decompose multi-class problems into several easier-to-solve binary sub-problems. One of the most popular binarization techniques is One versus One (OVO), which creates a sub-problem for each pair of classes of the original problem. Different versions of OVO...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/70776 |
| Acceso en línea: | http://hdl.handle.net/10810/70776 |
| Access Level: | acceso abierto |
| Palabra clave: | supervised classification decomposition strategies one versus one dynamic classifier selection |
| Sumario: | Class binarization techniques are used to decompose multi-class problems into several easier-to-solve binary sub-problems. One of the most popular binarization techniques is One versus One (OVO), which creates a sub-problem for each pair of classes of the original problem. Different versions of OVO have been developed to try to solve some of its problems, like DYNOVO, which dynamically tries to select the best classifier for each sub-problem. In this paper a new extension that has been made to DYNOVO, named PSEUDOVO, is presented, which also tries to avoid the non competent sub-problems. An empirical study has been carried out over several UCI data sets, as well as a new data set of musical pieces of well known classical composers. Promising results have been obtained, from which can be concluded that the extension that PSEUDOVO made to DYNOVO improves its performance. |
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