VHT: Vertical Hoeffding Tree

TL;DR

The paper introduces VHT, a novel distributed streaming decision tree algorithm utilizing vertical parallelism, capable of handling large-scale IoT data efficiently while maintaining high accuracy and scalability.

Contribution

It presents the first distributed streaming decision tree algorithm that combines distributed and streaming features using vertical parallelism.

Findings

VHT achieves high accuracy comparable to non-distributed trees.

VHT demonstrates superior throughput and scalability on real-world clusters.

The algorithm effectively handles large, high-speed IoT data streams.

Abstract

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 distributed data streams, and thus able to run on real-world clusters. We run several experiments to study the accuracy and throughput performance of our new VHT algorithm, as well as its ability to scale while keeping its superior performance with respect to non-distributed decision trees.