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Application of Machine Learning Techniques to Situational Risk Assessment Based on Accident Database

Authors:
Ryuta Watanabe
Keisuke Yamazaki
Tsuyoshi Nakajima

Keywords: Machine learning; Support Vector Machine; Bayesian Network; Risk Assessment

Abstract:
It is challenging to prevent various kinds of offenses, such as bicycle theft or street snatching. Machine learning techniques, like Support Vector Machine (SVM) and Bayesian Network (BN), are considered to be promising technologies to assess the risk of such offenses. However, applying these technologies is not easy and some problems include preparation of missing data, providing reasons, and multi –level classification of risks. In this paper, we propose a method to solve these problems. We applied our proposed method on an example of risk information provision system on bicycle parking lots; the results showed the effectiveness of the proposed method

Pages: 71 to 74

Copyright: Copyright (c) IARIA, 2018

Publication date: November 18, 2018

Published in: conference

ISSN: 2308-4278

ISBN: 978-1-61208-676-7

Location: Athens, Greece

Dates: from November 18, 2018 to November 22, 2018