Tree ensembles, such as Random Forest (RF), are popular methods in machine learning because of their efficiency and superior performance. However, they always grow big trees and large forests, which limits their use in many memory constrained applications. In this paper, we propose Random decision Directed Acyclic Graph (RDAG), which employs an entropy-based pre-pruning and node merging strategy to reduce the number of nodes in random forest. Empirical results show that the resulting model, which is a DAG, dramatically reduces the model size while achieving competitive classification performance when compared to RF.
CITATION STYLE
Liu, X., Liu, X., Lai, Y., Yang, F., & Zeng, Y. (2019). Random Decision DAG: An Entropy Based Compression Approach for Random Forest. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11448 LNCS, pp. 319–323). Springer Verlag. https://doi.org/10.1007/978-3-030-18590-9_37
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