Fusion of classifiers based on physical activities data from smartphone user

被引：1

作者：

Kadri, Nesrine ^{[1
,2
]}

Ellouze, Ameni ^{[3
]}

Ksantini, Mohamed ^{[3
]}

机构：

[1] Univ Sfax, CEM Lab, ENIS, Sfax, Tunisia

[2] Univ Sousse, ISIT COM, Sousse, Tunisia

[3] Univ Sfax, CEM Lab, ENIS, Dept Elect Engn, Sfax, Tunisia

来源：

PROCEEDINGS OF THE 2020 17TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS & DEVICES (SSD 2020) | 2020年

关键词：

physical activities recognition; Dempster-Shafer theory; belief functions; machine learning; smartphone sensors; EVIDENTIAL CALIBRATION; MULTIPLE CLASSIFIERS;

D O I：

10.1109/SSD49366.2020.9364266

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The behavior recognition of mobile phone users is based on different types of smartphone sensors. The accelerometer is one of these sensors which can represent the person activity. In this paper, in order to recognize and classify the physical activities of user's smartphones, we will use the Dempster-Shafer (DS) theory of belief functions. After applying decision tree machine learning algorithm on each of the three axis of movement, we will fuse different combinations of these results. Then, we will fuse different machine learning algorithms applied on three axis of movement together. We will show that using a classifier of each attribute give better results than applying on all attributes.

引用

页码：903 / 908

页数：6

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