Cross-Cluster Weighted Forests
This addresses the issue of dataset heterogeneity for applications in biological data analysis, such as cancer profiling, but is incremental as it builds on existing Random Forest methods.
The paper tackles the problem of handling clusters or batch effects in training datasets by proposing Cross-Cluster Weighted Forests, which ensemble Random Forest learners trained on clusters, resulting in significant improvements in accuracy and generalizability over traditional Random Forest.
Adapting machine learning algorithms to better handle the presence of clusters or batch effects within training datasets is important across a wide variety of biological applications. This article considers the effect of ensembling Random Forest learners trained on clusters within a dataset with heterogeneity in the distribution of the features. We find that constructing ensembles of forests trained on clusters determined by algorithms such as k-means results in significant improvements in accuracy and generalizability over the traditional Random Forest algorithm. We begin with a theoretical exploration of the benefits of our novel approach, denoted as the Cross-Cluster Weighted Forest, and subsequently empirically examine its robustness to various data-generating scenarios and outcome models. Furthermore, we explore the influence of the data partitioning and ensemble weighting strategies on the benefits of our method over the existing paradigm. Finally, we apply our approach to cancer molecular profiling and gene expression datasets that are naturally divisible into clusters and illustrate that our approach outperforms classic Random Forest.