Training big random forests with little resources
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Standard
Training big random forests with little resources. / Gieseke, Fabian; Igel, Christian.
KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Association for Computing Machinery, 2018. p. 1445-1454.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - Training big random forests with little resources
AU - Gieseke, Fabian
AU - Igel, Christian
PY - 2018
Y1 - 2018
N2 - Without access to large compute clusters, building random forests on large datasets is still a challenging problem. This is, in particular, the case if fully-grown trees are desired. We propose a simple yet effective framework that allows to efficiently construct ensembles of huge trees for hundreds of millions or even billions of training instances using a cheap desktop computer with commodity hardware. The basic idea is to consider a multi-level construction scheme, which builds top trees for small random subsets of the available data and which subsequently distributes all training instances to the top trees' leaves for further processing. While being conceptually simple, the overall efficiency crucially depends on the particular implementation of the different phases. The practical merits of our approach are demonstrated using dense datasets with hundreds of millions of training instances.
AB - Without access to large compute clusters, building random forests on large datasets is still a challenging problem. This is, in particular, the case if fully-grown trees are desired. We propose a simple yet effective framework that allows to efficiently construct ensembles of huge trees for hundreds of millions or even billions of training instances using a cheap desktop computer with commodity hardware. The basic idea is to consider a multi-level construction scheme, which builds top trees for small random subsets of the available data and which subsequently distributes all training instances to the top trees' leaves for further processing. While being conceptually simple, the overall efficiency crucially depends on the particular implementation of the different phases. The practical merits of our approach are demonstrated using dense datasets with hundreds of millions of training instances.
KW - Classification
KW - Ensemble methods
KW - Large-scale data analytics
KW - Machine learning
KW - Random forests
KW - Regression trees
UR - http://www.scopus.com/inward/record.url?scp=85051471641&partnerID=8YFLogxK
U2 - 10.1145/3219819.3220124
DO - 10.1145/3219819.3220124
M3 - Article in proceedings
AN - SCOPUS:85051471641
SN - 9781450355520
SP - 1445
EP - 1454
BT - KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - ACM Association for Computing Machinery
T2 - 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
Y2 - 19 August 2018 through 23 August 2018
ER -
ID: 202618778