Extremely Simple Streaming Forest

TL;DR

The paper introduces XForest, a straightforward streaming decision forest method that incrementally updates trees with new data, often matching or surpassing batch algorithms in accuracy and efficiency across diverse datasets.

Contribution

Proposes a simple extension to decision trees for streaming data, replacing old trees with new ones, improving accuracy and efficiency over existing complex methods.

Findings

XForest performs comparably or better than batch algorithms on 72 classification problems.

The zero-added-node extension allows efficient transfer to new tasks using only inference.

XForest offers a simple, effective standard for streaming decision forests.

Abstract

Decision forests, including random forests and gradient boosting trees, remain the leading machine learning methods for many real-world data problems, especially on tabular data. However, most of the current implementations only operate in batch mode, and therefore cannot incrementally update when more data arrive. Several previous works developed streaming trees and ensembles to overcome this limitation. Nonetheless, we found that those state-of-the-art algorithms suffer from a number of drawbacks, including low accuracy on some problems and high memory usage on others. We therefore developed an extremely simple extension of decision trees: given new data, simply update existing trees by continuing to grow them, and replace some old trees with new ones to control the total number of trees. In a benchmark suite containing 72 classification problems (the OpenML-CC18 data suite), we illustrate that our approach, $\textit{Extremely Simple Streaming Forest}$ (XForest), does not suffer from either of the aforementioned limitations. On those datasets, we also demonstrate that our approach often performs as well as, and sometimes even better than, conventional batch decision forest algorithms. With a $\textit{zero-added-node}$ approach, XForest-Zero, we also further extend existing splits to new tasks, and this very efficient method only requires inference time. Thus, XForests establish a simple standard for streaming trees and forests that could readily be applied to many real-world problems.