PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce
Summary: PLANET leverages MapReduce to train tree ensembles on massive datasets using commodity hardware. It frames tree learning as distributed MapReduce steps, enabling scalable construction of classification/regression trees and ensembles on commodity clusters, demonstrated on computational advertising. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Biswanath Panda (Google)
- 2. Joshua S. Herbach (Google)
- 3. Sugato Basu (Google)
- 4. Roberto J. Bayardo (Google)
BibTeX Citation
@article{panda_vldb09,
title = {{PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce}},
author = {Panda, Biswanath and Herbach, Joshua S. and Basu, Sugato and Bayardo, Roberto J.},
journal = {PVLDB},
series = {{VLDB} '09},
volume = {2},
number = {1},
pages = {1426--1437},
doi = {10.14778/1687627.1687632},
url = {https://doi.org/10.14778/1687627.1687632},
year = {2009}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 443 | Random Sampling Techniques for Space Efficient Online Computation of Order Statistics of Large Datasets | 1999 | SIGMOD | 0.00018373044 |
| 1,485 | SPRINT: A Scalable Parallel Classifier for Data Mining | 1996 | VLDB | 0.00010628998 |
| 1,994 | RainForest - A Framework for Fast Decision Tree Construction of Large Datasets | 1998 | VLDB | 9.3409624e-05 |
| 2,810 | BOAT—Optimistic Decision Tree Construction | 1999 | SIGMOD | 8.0985732e-05 |
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