Abstract
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.
| Original language | English |
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| Title of host publication | KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Publisher | ACM Association for Computing Machinery |
| Publication date | 2018 |
| Pages | 1445-1454 |
| ISBN (Print) | 9781450355520 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 - London, United Kingdom Duration: 19 Aug 2018 → 23 Aug 2018 |
Conference
| Conference | 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 |
|---|---|
| Country/Territory | United Kingdom |
| City | London |
| Period | 19/08/2018 → 23/08/2018 |
| Sponsor | ACM SIGKDD, ACM SIGMOD |
Keywords
- Classification
- Ensemble methods
- Large-scale data analytics
- Machine learning
- Random forests
- Regression trees