NDLI: Interpretable models from distributed data via merging of decision trees

Content Provider	IEEE Xplore Digital Library
Author	Andrzejak, A. Langner, F. Zabala, S.
Copyright Year	2013
Description	Author affiliation: PVS, Heidelberg Univ., Heidelberg, Germany (Andrzejak, A.; Langner, F.; Zabala, S.)
Abstract	Learning from distributed data becomes increasingly important. Factors contributing to this trend include emergence of data sets exceeding RAM sizes and inherently distributed scenarios such as mobile environments. Also in these cases interpretable models are favored: they facilitate identifying artifacts and understanding the impact of individual variables. Given the distributed environment, even if the individual learner on each site is interpretable, the overall model usually is not (as e.g. in case of voting schemes). To overcome this problem we propose an approach for efficient merging of decision trees (each learned independently) into a single decision tree. The method complements the existing distributed decision trees algorithms by providing interpretable intermediate models and tolerating constraints on bandwidth and RAM size. The latter properties are achieved by trading RAM and communication constraints for accuracy. Our method and the mentioned trade-offs are validated in experiments on real-world data sets.
Starting Page	1
Ending Page	9
File Size	376093
Page Count	9
File Format	PDF
ISBN	9781467358958
DOI	10.1109/CIDM.2013.6597210
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-16
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Program processors Accuracy Handheld computers Merging Distributed databases Vectors Decision trees
Content Type	Text
Resource Type	Article

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