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

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

研究成果: 書貢獻/報告類型會議論文篇章同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
主出版物標題Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020
發行者Association for Computing Machinery
ISBN(電子)9781450370530
DOIs
出版狀態已出版 - 10 8月 2020
事件2020 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020 - Virtual, Online, United States
持續時間: 10 8月 202012 8月 2020

出版系列

名字ACM International Conference Proceeding Series

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???event.eventtypes.event.conference???2020 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020
國家/地區United States
城市Virtual, Online
期間10/08/2012/08/20

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