Tree in Tree: from Decision Trees to Decision Graphs

TL;DR

This paper introduces Tree in Tree (TnT), a decision graph framework that extends decision trees into directed acyclic graphs, offering improved accuracy and efficiency for classification tasks.

Contribution

The paper presents TnT, a novel recursive decision graph construction method that outperforms traditional decision trees in accuracy and size, suitable for large datasets.

Findings

TnT achieves better classification accuracy than decision trees.

TnT reduces model size while maintaining performance.

TnT can be effectively used in ensemble methods.

Abstract

Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree in Tree decision graph (TnT), a framework that extends the conventional decision tree to a more generic and powerful directed acyclic graph. TnT constructs decision graphs by recursively growing decision trees inside the internal or leaf nodes instead of greedy training. The time complexity of TnT is linear to the number of nodes in the graph, and it can construct decision graphs on large datasets. Compared to decision trees, we show that TnT achieves better classification performance with reduced model size, both as a stand-alone classifier and as a base estimator in bagging/AdaBoost ensembles. Our proposed model is a novel, more efficient, and accurate alternative to the widely-used decision trees.