Tree in Tree: from Decision Trees to Decision Graphs
This provides a more efficient and accurate alternative to decision trees for classification tasks, though it appears incremental as it builds on existing tree structures.
The paper tackles the limitation of decision trees by introducing Tree in Tree decision graphs (TnT), which recursively grow trees inside nodes to form directed acyclic graphs, achieving better classification performance with reduced model size, as shown in experiments.
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.