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This research presents a new learning model, the Parallel Decision DAG (PDDAG), and shows how to use it to represent an ensemble of decision trees while using significantly less storage. Ensembles such as Bagging and Boosting have a high probability of encoding redundant data structures, and PDDAGs provide a way to remove this redundancy in decision tree based ensembles. When trained by encoding an ensemble, the new model behaves similar to the original ensemble, and can be made to perform identically to it. The reduced storage requirements allow an ensemble approach to be used in cases where storage requirements would normally be exceeded, and the smaller model can potentially execute faster by reducing redundant computation.

REDUCING DECISION TREE ENSEMBLE SIZE USING PARALLEL DECISION DAGS