Learning Ensembles of Process-Based Models by Bagging of Random Library Samples

Abstract

We propose a new method for learning ensembles of process-based models for predictive modeling of dynamic systems from data and knowledge. Previous work has shown that ensembles based on sampling data (i.e., bagging), significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial computational overhead needed for learning. On the other hand, methods for constructing ensembles based on sampling knowledge (i.e., random library samples, RLS) allow for efficient learning ensembles of process-based models, while maintaining their long-term predictive performance. This paper aims at checking the conjecture whether the combination of these methods has a potential for further performance improvements. The proposed method, bagging of random library samples for learning ensembles of process-based models combines the afore-mentioned approaches in terms of sampling both data and knowledge. We apply the method to and evaluate its performance on a set of automated predictive modeling tasks in two lake ecosystems from data and library of knowledge for modeling population dynamics. The experimental results serve both to identify the optimal design decisions regarding the proposed method as well as to asses its predictive ability. The results show that such ensembles outperform single process-based model, but also outperform each of the two methods for learning ensembles from data samples (bagging) and knowledge samples (RLS).