School violence detection based on multi-sensor fusion and improved relief-F algorithms
Ye, Liang; Shi, Jifu; Ferdinando, Hany; Seppänen, Tapio; Alasaarela, Esko (2019-07-05)
Ye L., Shi J., Ferdinando H., Seppänen T., Alasaarela E. (2019) School Violence Detection Based on Multi-sensor Fusion and Improved Relief-F Algorithms. In: Han S., Ye L., Meng W. (eds) Artificial Intelligence for Communications and Networks. AICON 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 287. Springer, Cham
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019. This is a post-peer-review, pre-copyedit version of an article published in Artiﬁcial intelligence for communications and networks : First EAI International Conference, AICON 2019 Harbin, China, May 25–26, 2019, Proceedings, Part II. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-22971-9_22.
School bullying is a common social problem around the world, and school violence is considered to be the most harmful form of school bullying. This paper proposes a school violence detecting method based on multi-sensor fusion and improved Relief-F algorithms. Data are gathered with two movement sensors by role playing of school violence and daily-life activities. Altogether 9 kinds of activities are recorded. Time domain features and frequency domain features are extracted and filtered by an improved Relief-F algorithm. Then the authors build a two-level classifier. The first level is a Decision Tree classifier which separates the activity of jump from the others, and the second level is a Radial Basis Function neural network which classifies the remainder 8 kinds of activities. Finally a decision layer fusion algorithm combines the recognition results of the two sensors together. The average recognition accuracy of school violence reaches 84.4%, and that of daily-life reaches 97.3%.
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