Ensemble of Competitive Associative Nets and Multiple K-fold Cross-Validation for Estimating Predictive Uncertainty in Environmental Modelling
Shuichi Kurogi, Daisuke Kuwahara, S. Tanaka · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
This article describes the method which we have used for the predictive uncertainty in environmental modelling competition. The method uses competitive associative net called CAN2 embedding piecewise linear approximation scheme. With the scheme, we can naturally estimate piecewise error distribution or heteroscedastic error distribution which may be caused by the noise involved in environmental data. For improving the CAN2 with an efficient batch learning method for reducing empirical (training) error, we introduce ensemble method for more accurate prediction or less prediction (generalization) error. We also introduce multiple K-fold cross-validation for obtaining reliable predictive distribution.