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

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

doi:10.1007/978-3-030-18590-9_37

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 proceeding › Conference 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 language	English
Title of host publication	Database Systems for Advanced Applications - DASFAA 2019 International Workshops
Subtitle of host publication	BDMS, BDQM, and GDMA, Proceedings
Editors	Guoliang Li, Joao Gama, Yongxin Tong, Jun Yang, Juggapong Natwichai
Publisher	Springer Verlag
Pages	319-323
Number of pages	5
ISBN (Print)	9783030185893
DOIs	https://doi.org/10.1007/978-3-030-18590-9_37
Publication status	Published - 24 Apr 2019
Event	24th International Conference on Database Systems for Advanced Applications - Chiang Mai, Thailand Duration: 22 Apr 2019 → 25 Apr 2019 Conference number: 24

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11448 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Database Systems for Advanced Applications
Abbreviated title	DASFAA 2019
Country/Territory	Thailand
City	Chiang Mai
Period	22/04/19 → 25/04/19

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10.1007/978-3-030-18590-9_37

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

Liu, Xin ; Liu, Xiao ; Lai, Yongxuan et al. / Random Decision DAG : An Entropy Based Compression Approach for Random Forest. Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. editor / Guoliang Li ; Joao Gama ; Yongxin Tong ; Jun Yang ; Juggapong Natwichai. Springer Verlag, 2019. pp. 319-323 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ebbef19144ed4070b40f17fb8025bfc1,

title = "Random Decision DAG: An Entropy Based Compression Approach for Random Forest",

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.",

author = "Xin Liu and Xiao Liu and Yongxuan Lai and Fan Yang and Yifeng Zeng",

year = "2019",

month = apr,

day = "24",

doi = "10.1007/978-3-030-18590-9_37",

language = "English",

isbn = "9783030185893",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "319--323",

editor = "Guoliang Li and Joao Gama and Yongxin Tong and Jun Yang and Juggapong Natwichai",

booktitle = "Database Systems for Advanced Applications - DASFAA 2019 International Workshops",

address = "Germany",

note = "24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 ; Conference date: 22-04-2019 Through 25-04-2019",

}

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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11448 LNCS, Springer Verlag, pp. 319-323, 24th International Conference on Database Systems for Advanced Applications, Chiang Mai, Thailand, 22/04/19. https://doi.org/10.1007/978-3-030-18590-9_37

Random Decision DAG: An Entropy Based Compression Approach for Random Forest. / Liu, Xin; Liu, Xiao; Lai, Yongxuan et al.
Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. ed. / Guoliang Li; Joao Gama; Yongxin Tong; Jun Yang; Juggapong Natwichai. Springer Verlag, 2019. p. 319-323 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11448 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Random Decision DAG

T2 - 24th International Conference on Database Systems for Advanced Applications

AU - Liu, Xin

AU - Liu, Xiao

AU - Lai, Yongxuan

AU - Yang, Fan

AU - Zeng, Yifeng

N1 - Conference code: 24

PY - 2019/4/24

Y1 - 2019/4/24

N2 - 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.

AB - 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.

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U2 - 10.1007/978-3-030-18590-9_37

DO - 10.1007/978-3-030-18590-9_37

M3 - Conference contribution

AN - SCOPUS:85065409501

SN - 9783030185893

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 319

EP - 323

BT - Database Systems for Advanced Applications - DASFAA 2019 International Workshops

A2 - Li, Guoliang

A2 - Gama, Joao

A2 - Tong, Yongxin

A2 - Yang, Jun

A2 - Natwichai, Juggapong

PB - Springer Verlag

Y2 - 22 April 2019 through 25 April 2019

ER -

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

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

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