VHT: Vertical hoeffding tree
By Nicolas Kourtellis, Gianmarco De Francisci Morales, Albert Bifet, Arinto Murdopo. 2016
55 citations recorded by the source corpus.
IoT big data requires new machine learning methods able to scale to large size of data arriving at high speed. Decision trees are popular machine learning models since they are very effective, yet easy to interpret and visualize. In the literature, we can find distributed algorithms for learning decision trees, and also streaming algorithms, but not algorithms that combine both features. In this paper we present the Vertical Hoeffding Tree (VHT), the first distributed streaming algorithm for learning decision trees. It features a novel way of distributing decision trees via vertical parallelism. The algorithm is implemented on top of Apache SAMOA, a platform for mining big data streams, and thus able to run on real-world clusters. Our experiments to study the accuracy and throughput of VHT prove its ability to scale while attaining superior performance compared to sequential decision trees.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Praneeth Vepakomma
- Abhishek Singh
- Ramesh Raskar
- Vivek Sharma
- Róbert Pálovics
- András A. Benczúr
- Levente Kocsis
- Heitor Murilo Gomes
Citations in the corpus
- SplitNN-driven Vertical Partitioning (2020, background): “Vertically partitioned decision trees The works in (Gascón et al. , 2016; Vaidya et al. , 2008; Vaidya & Clifton, 2004; Kourtellis et al. , 2016; Cheng et al. , 2019) share approaches for vertically partitioned learning with decision trees, gradient boosted decision trees and Hoeffding trees.”
- Online Machine Learning in Big Data Streams (2018, methodology): “ the new child nodes are computed based on the new data from the stream. Similar methods for regression trees are described in [13, 120]. For online vertical parallel distributed Hoeffding Trees, see [141]. One problem in decision tree construction on streaming data is the cost of maintaining attrib”
- A Survey on Ensemble Learning for Data Stream Classification (2017, methodology): “Therefore, in the near future we may see more and more classifiers developed for big data stream processing, such as the Streaming Parallel Decision Tree [Ben-Haim and Tom-Tov 2010], HSMiner [Haque et al. 2014] and Vertical Hoeffding Tree (VHT) [Kourtellis et al. 2016].”