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Lookup NU author(s): Dr Jie ZhangORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Ensemble learning systems (ELS) have been widely utilized for human activity recognition (HAR) with multiple homogeneous or heterogeneous sensors. However, traditional ensemble approaches for HAR cannot always work well due to insufficient accuracy and diversity of base classifiers, absence of ensemble pruning, as well as the inefficiency of fusion strategy. To overcome these problems, this paper proposes a novel selective ensemble approach with group decision making (GDM) for decision-level fusion in HAR. As a result, the fusion process in the ELS is transformed into an abstract process that includes individual experts (base classifiers) making decisions with the GDM fusion strategy. Firstly, a set of diverse local base classifiers are constructed through the corresponding mechanism of the base classifier and the sensor. Secondly, the pruning methods and the number of selected base classifiers for the fusion phase are determined by considering the diversity among base classifiers and the accuracy of candidate classifiers. Two ensemble pruning methods are utilized: mixed diversity measure and complementarity measure. Thirdly, component decision information from the selected base classifiers is combined by using the GDM fusion strategy and the recognition results of the HAR approach can be obtained. Experimental results on two public activity recognition datasets (The OPPORTUNITY dataset; Daily and Sports Activity Dataset (DSAD)) suggest that the proposed GDM-based approach outperforms the well-known fusion techniques and other and start-of-the-art approaches in the literature.
Author(s): Tian Y, Zhang J, Chen Q, Hou S, Xiao L
Publication type: Article
Publication status: Published
Journal: Sensors
Year: 2022
Volume: 22
Issue: 21
Online publication date: 27/10/2022
Acceptance date: 25/10/2022
Date deposited: 09/11/2022
ISSN (electronic): 2297-8739
Publisher: MDPI
URL: https://doi.org/10.3390/s22218225
DOI: 10.3390/s22218225
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