Online Meta-Forest for Regression Data Streams
Ammar Shaker, Christoph Gärtner, Xiao Yang He, Shujian Yu · 2020
Stream learning is essential when there is limited memory, time and computational power. However, existing streaming methods are mostly designed for classification with only a few exceptions for regression problems. Although being fast, the performance of these online regression methods is inadequate due to their dependence on merely linear models. Besides, only a few stream methods are based on meta-learning that aims at facilitating the dynamic choice of the right model. Nevertheless, these approaches are restricted to recommend learners on a window and not on the instance level. In this paper, we present a novel approach, named Online Meta-Forest, that incrementally induces an ensemble of meta-learners that selects the best set of predictors for each test example. Each meta-learner has the ability to find a non-linear mapping of the input space to the set of induced models. We conduct a series of experiments demonstrating that Online Meta-Forest outperforms related methods on 16 out of 25 evaluated benchmark and domain datasets in transportation.