Energy-Aware Very Fast Decision Tree
| Autor: | Niklas Lavesson, Eva García-Martín, Veselka Boeva, Håkan Grahn, Emiliano Casalicchio |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Concept drift
Computer science Green artificial intelligence Decision tree 02 engineering and technology Energy-aware machine learning 020204 information systems 0202 electrical engineering electronic engineering information engineering Computer Sciences Applied Mathematics Data stream mining Hoeffding trees Energy consumption Computer Science Applications Energy efficiency Datavetenskap (datalogi) Computational Theory and Mathematics Computer engineering Modeling and Simulation 020201 artificial intelligence & image processing Algorithm design Mobile device Streaming algorithm Energy (signal processing) Information Systems Efficient energy use |
| Popis: | Recently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting the nmin adaptation method, which reduces the energy consumption of the VFDT algorithm with only minor effects on accuracy. nmin adaptation allows the algorithm to grow faster in those branches where there is more confidence to create a split, and delays the split on the less confident branches. This removes unnecessary computations related to checking for splits but maintains similar levels of accuracy. We have conducted extensive experiments on 29 public datasets, showing that the VFDT with nmin adaptation consumes up to 31% less energy than the original VFDT, and up to 96% less energy than the CVFDT (VFDT adapted for concept drift scenarios), trading off up to 1.7 percent of accuracy. |
| Databáze: | OpenAIRE |
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