Random Decision DAG: An Entropy Based Compression Approach for Random Forest

Xin Liu, Xiao Liu, Yongxuan Lai, Fan Yang, Yifeng Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - DASFAA 2019 International Workshops
Subtitle of host publicationBDMS, BDQM, and GDMA, Proceedings
EditorsGuoliang Li, Joao Gama, Yongxin Tong, Jun Yang, Juggapong Natwichai
PublisherSpringer Verlag
Pages319-323
Number of pages5
ISBN (Print)9783030185893
DOIs
Publication statusPublished - 24 Apr 2019
Event24th International Conference on Database Systems for Advanced Applications - Chiang Mai, Thailand
Duration: 22 Apr 201925 Apr 2019
Conference number: 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11448 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Database Systems for Advanced Applications
Abbreviated titleDASFAA 2019
CountryThailand
CityChiang Mai
Period22/04/1925/04/19

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  • Cite this

    Liu, X., Liu, X., Lai, Y., Yang, F., & Zeng, Y. (2019). Random Decision DAG: An Entropy Based Compression Approach for Random Forest. In G. Li, J. Gama, Y. Tong, J. Yang, & J. Natwichai (Eds.), Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings (pp. 319-323). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11448 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-18590-9_37