How to cultivate a green decision tree without loss of accuracy?

Tseng Yi Chen, Yuan Hao Chang, Ming Chang Yang, Huang Wei Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Decision tree is the core algorithm of the random forest learning that has been widely applied to classification and regression problems in the machine learning field. For avoiding underfitting, a decision tree algorithm will stop growing its tree model when the model is a fully-grown tree. However, a fully-grown tree will result in an overfitting problem reducing the accuracy of a decision tree. In such a dilemma, some post-pruning strategies have been proposed to reduce the model complexity of the fully-grown decision tree. Nevertheless, such a process is very energy-inefficiency over an non-volatile-memory-based (NVM-based) system because NVM generally have high writing costs (i.e., energy consumption and I/O latency). Such unnecessary data will induce high writing energy consumption and long I/O latency on NVM-based architectures, especially for low-power-oriented embedded systems. In order to establish a green decision tree (i.e., a tree model with minimized construction energy consumption), this study rethinks a pruning algorithm, namely duo-phase pruning framework, which can significantly decrease the energy consumption on the NVM-based computing system without loss of accuracy.

Original languageEnglish
Title of host publicationProceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450370530
DOIs
StatePublished - 10 Aug 2020
Event2020 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020 - Virtual, Online, United States
Duration: 10 Aug 202012 Aug 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2020 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020
Country/TerritoryUnited States
CityVirtual, Online
Period10/08/2012/08/20

Keywords

  • decision tree
  • multi-write NVRAM
  • pruning strategy

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