Evaluating the Four-Way Performance Trade-Off for Data Stream Classification in Edge Computing

Lopes, Jessica Fernandes; Santana, Everton José; Costa, Victor G. Turrisi da; Zarpelão, Bruno Bogaz; Barbon, Sylvio

doi:10.1109/tnsm.2020.2983921

Cited by 8 publications

(3 citation statements)

References 45 publications

(56 reference statements)

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“…The authors in [ 9 ] have analyzed the resource trade-offs of six online decision trees applied to edge computing. Their results showed that the Very Fast Decision Tree and the Strict Very Fast Decision Tree were the most energy friendly, the latter having the smallest memory footprint.…”

Section: Related Workmentioning

confidence: 99%

A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects

Khannouz

Glatard

2020

Sensors

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This paper evaluates data stream classifiers from the perspective of connected devices, focusing on the use case of Human Activity Recognition. We measure both the classification performance and resource consumption (runtime, memory, and power) of five usual stream classification algorithms, implemented in a consistent library, and applied to two real human activity datasets and three synthetic datasets. Regarding classification performance, the results show the overall superiority of the Hoeffding Tree, the Mondrian forest, and the Naïve Bayes classifiers over the Feedforward Neural Network and the Micro Cluster Nearest Neighbor classifiers on four datasets out of six, including the real ones. In addition, the Hoeffding Tree and—to some extent—the Micro Cluster Nearest Neighbor, are the only classifiers that can recover from a concept drift. Overall, the three leading classifiers still perform substantially worse than an offline classifier on the real datasets. Regarding resource consumption, the Hoeffding Tree and the Mondrian forest are the most memory intensive and have the longest runtime; however, no difference in power consumption is found between classifiers. We conclude that stream learning for Human Activity Recognition on connected objects is challenged by two factors which could lead to interesting future work: a high memory consumption and low F1 scores overall.

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Section: Related Workmentioning

confidence: 99%

A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects

Khannouz

Glatard

2020

Sensors

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“…The authors in [11] have analyzed the resource trade-offs of six online decision trees applied to edge computing. Their results showed that the Very Fast Decision Tree and the Strict Very Fast Decision Tree were the most energy-friendly, the latter having the smallest memory footprint.…”

Section: A Comparisons Of Data Stream Classifiersmentioning

confidence: 99%

A benchmark of data stream classification for human activity recognition on connected objects

Khannouz¹,

Glatard²

2020

Preprint

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This paper evaluates data stream classifiers from the perspective of connected devices, focusing on the use case of HAR. We measure both classification performance and resource consumption (runtime, memory, and power) of five usual stream classification algorithms, implemented in a consistent library, and applied to two real human activity datasets and to three synthetic datasets. Regarding classification performance, results show an overall superiority of the HT, the MF, and the NB classifiers over the FNN and the Micro Cluster Nearest Neighbor (MCNN) classifiers on 4 datasets out of 6, including the real ones. In addition, the HT, and to some extent MCNN, are the only classifiers that can recover from a concept drift. Overall, the three leading classifiers still perform substantially lower than an offline classifier on the real datasets. Regarding resource consumption, the HT and the MF are the most memory intensive and have the longest runtime, however, no difference in power consumption is found between classifiers. We conclude that stream learning for HAR on connected objects is challenged by two factors which could lead to interesting future work: a high memory consumption and low F1 scores overall.

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“…Many algorithms for data stream classification have been proposed [19][20][21]. Although some of these algorithms have been studied in multiple fields [16,[22][23][24][25], the evaluation of their performance and the trade-offs involved in their performance metrics and memory costs have not been addressed for VEC environments. Hence, this work seeks to identify which online data stream classification algorithms may be suitable for VEC environments through rigorous comparative evaluation.…”

Section: Introductionmentioning

confidence: 99%

Data Stream Classification Algorithms for Workload Orchestration in Vehicular Edge Computing: A Comparative Evaluation

Al-Tarawneh¹

2021

IJFIS

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This paper reports on the use of online data stream classification algorithms to support workload orchestration in vehicular edge computing environments. These algorithms can be used to predict the ability of available computational nodes to successfully handle computational tasks generated from vehicular applications. Several online data stream classification algorithms have been evaluated based on synthetic datasets generated from simulated vehicular edge computing environments. In addition, a multi-criteria decision analysis technique was utilized to rank the different algorithms based on their performance metrics. The evaluation results demonstrate that the considered algorithms can handle online classification operations with various trade-offs and dominance relations with respect to their obtained performance. In addition, the utilized multi-criteria decision analysis technique can efficiently rank various algorithms and identify the most appropriate algorithms to augment workload orchestration. Furthermore, the evaluation results show that the leveraging bagging algorithm, with an extremely fast decision tree base estimator, is able to maintain marked online classification performance and persistent competitive ranking among its counterparts for all datasets. Hence, it can be considered a promising choice to reinforce workload orchestration in vehicular edge computing environments.

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Evaluating the Four-Way Performance Trade-Off for Data Stream Classification in Edge Computing

Cited by 8 publications

References 45 publications

A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects

A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects

A benchmark of data stream classification for human activity recognition on connected objects

Data Stream Classification Algorithms for Workload Orchestration in Vehicular Edge Computing: A Comparative Evaluation

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