Paper Info
Title
Lossless (and Lossy) Compression of Random Forests.
Abstract
Ensemble methods are among the state-of-the-art predictive modeling approaches. Applied to modern big data, these methods often require a large number of sub-learners, where the complexity of each learner typically grows with the size of the dataset. This phenomenon results in an increasing demand for storage space, which may be very costly. This problem mostly manifests in a subscriber based environment, where a user-specific ensemble needs to be stored on a personal device with strict storage limitations (such as a cellular device). In this work we introduce a novel method for lossless compression of tree-based ensemble methods, focusing on random forests. Our suggested method is based on probabilistic modeling of the ensembleu0027s trees, followed by model clustering via Bregman divergence. This allows us to find a minimal set of models that provides an accurate description of the trees, and at the same time is small enough to store and maintain. Our compression scheme demonstrates high compression rates on a variety of modern datasets. Importantly, our scheme enables predictions from the compressed format and a perfect reconstruction of the original ensemble. In addition, we introduce a theoretically sound lossy compression scheme, which allows us to control the trade-off between the distortion and the coding rate.
Year
Venue
Field
2018
arXiv: Learning
Lossy compression,Algorithm,Bregman divergence,Artificial intelligence,Probabilistic logic,Random forest,Cluster analysis,Distortion,Ensemble learning,Machine learning,Mathematics,Lossless compression
DocType
Volume
Citations 
Journal
abs/1810.11197
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Amichai Painsky1188.11
Saharon Rosset21087105.33