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CIDM
2013
IEEE

Interpretable models from distributed data via merging of decision trees

8 years 10 months ago
Interpretable models from distributed data via merging of decision trees
Abstract—Learning in parallel or 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 parallel 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 trad...
Artur Andrzejak, Felix Langner, Silvestre Zabala
Added 19 May 2015
Updated 19 May 2015
Type Journal
Year 2013
Where CIDM
Authors Artur Andrzejak, Felix Langner, Silvestre Zabala
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