書誌事項
- 公開日
- 2001-10
- 権利情報
-
- https://www.elsevier.com/tdm/userlicense/1.0/
- https://www.elsevier.com/legal/tdmrep-license
- DOI
-
- 10.1016/s0377-2217(00)00244-7
- 公開者
- Elsevier BV
この論文をさがす
説明
Abstract This paper presents a technical framework to assess the impact of re-sampling on the ability of a supervised learning to correctly learn a classification problem. We use the bootstrap expression of the prediction error to identify the optimal re-sampling proportions in binary classification experiments using artificial neural networks. Based on Bayes decision rule and the a priori distribution of the objective data, an estimate for the optimal re-sampling proportion is derived as well as upper and lower bounds for the exact optimal proportion. The analytical considerations to extend the present method to cross-validation and multiple classes are also illustrated.
収録刊行物
-
- European Journal of Operational Research
-
European Journal of Operational Research 134 (1), 141-156, 2001-10
Elsevier BV