Paper Info
Title
Evaluating The Four-Way Performance Trade-Off For Stream Classification
Abstract
Machine Learning (ML) solutions need to deal efficiently with a huge amount of data available, addressing scalability concerns without sacrificing predictive performance. Moreover, this data comes in the form of a continuous and evolving stream imposing new constraints, e.g., limited memory and energy resources. In the same way, energy-aware ML algorithms are gaining relevance due to the power constraints of hardware platforms in several real-life applications, as the Internet of Things (IoT). Many algorithms have been proposed to cope with the mutable nature of data streams, with the Very Fast Decision Tree (VFDT) being one of the most widely used. An adaptation of the VFDT, called Strict VFDT (SVFDT), can significantly reduce memory usage without putting aside the predictive performance and time efficiency. However, the analysis of energy consumption regarding data stream processing of the VFDT and SVFDT is overlooked. In this work, we compare the four-way relationship between predictive performance, memory costs, time efficiency and energy consumption, tuning the hyperparameters of the algorithms to optimise the resources devoted to it. Experiments over 23 benchmark datasets revealed that the SVFDT-I is the most energy-friendly algorithm and greatly reduced memory consumption, being statistically superior to the VFDT.
Year
DOI
Venue
2019
10.1007/978-3-030-19223-5_1
GREEN, PERVASIVE, AND CLOUD COMPUTING, GPC 2019
Keywords
Field
DocType
Machine Learning, Data stream mining, Energy efficiency
Decision tree,Data stream mining,Hyperparameter,Computer science,Efficient energy use,Trade-off,Artificial intelligence,Energy resources,Energy consumption,Machine learning,Scalability,Distributed computing
Conference
Volume
ISSN
Citations 
11484
0302-9743
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Victor T. da Costa1295.90
Everton Jose Santana232.11
Jessica F. Lopes300.34
Sylvio Barbon44610.97