Curvas ROC y vecinos cercanos, propuesta de un nuevo algoritmo de condensación
保存先:
| 著者: | , |
|---|---|
| フォーマット: | artículo original |
| 状態: | Versión publicada |
| 出版日付: | 2011 |
| その他の書誌記述: | k-NN criteria are non parametric methods of statistical classificaction. They are accurate, versatile and distribution free. However, their computational cost may be too expensive; especially for large sample sizes. We present a new condensation algorithm based on the Binormal model for ROC curves. It transforms the training sample into a small set of low dimensional vetors. Contrasting with other condensation techniques described in the literature, our proposal helps to control the exchange of accuracy for condensation onthe training sample. The results of a Monte Carlo study show that its performance can be very competitive in different realistic scenarios, resulting in better training samples than other frequently used methods. |
| 国: | Portal de Revistas UCR |
| 機関: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| 言語: | Español |
| OAI Identifier: | oai:archivo.portal.ucr.ac.cr:article/2112 |
| オンライン･アクセス: | https://archivo.revistas.ucr.ac.cr/index.php/matematica/article/view/2112 |
| キーワード: | statistical classification area under the ROC curve nearest neighbours condensation Monte Carlo clasificación estadística área bajo la curva ROC modelo binormal vecinos cercanos condensación |