Paper Info
Title
A Communication-Efficient Parallel Algorithm for Decision Tree.
Abstract
Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Forest) is a widely used machine learning algorithm, due to its practical effectiveness and model interpretability. With the emergence of big data, there is an increasing need to parallelize the training process of decision tree. However, most existing attempts along this line suffer from high communication costs. In this paper, we propose a new algorithm, called Parallel Voting Decision Tree (PV-Tree), to tackle this challenge. After partitioning the training data onto a number of (e.g., M) machines, this algorithm performs both local voting and global voting in each iteration. For local voting, the top-k attributes are selected from each machine according to its local data. Then, globally top-2k attributes are determined by a majority voting among these local candidates. Finally, the full-grained histograms of the globally top-2k attributes are collected from local machines in order to identify the best (most informative) attribute and its split point. PV-Tree can achieve a very low communication cost (independent of the total number of attributes) and thus can scale out very well. Furthermore, theoretical analysis shows that this algorithm can learn a near optimal decision tree, since it can find the best attribute with a large probability. Our experiments on real-world datasets show that PV-Tree significantly outperforms the existing parallel decision tree algorithms in the trade-off between accuracy and efficiency.
Year
Venue
DocType
2016
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016)
Conference
Volume
ISSN
Citations 
29
1049-5258
1
PageRank 
References 
Authors
0.35
0
7
Name
Order
Citations
PageRank
Qi Meng1522.27
Ke, Guolin2655.36
Taifeng Wang317913.33
Wei Chen43416170.71
Ye, Qiwei5461.48
Zhi-Ming Ma622718.26
Tie-yan Liu74662256.32