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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)

Paper ID
10096
Venue
VLDB
Year
2009
Pagerank
8.4143663e-05
Overall Rank
2,560 | 82.44%
DOI
10.14778/1687627.1687632

Incoming Non-self Citations Over Time

Authors

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}
}

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