Prediction quality of Bayesian belief network model for risky behavior: comparison of subsamples with different rates

DOI

10.2991/eusflat-19.2019.90How to use a DOI?

Keywords

Bayesian Belief Network machine learning behavior models risky behavior

Abstract

The study investigates the proposed approach for behavior modeling on the base of Bayesian belief networks that allows predicting behavior characteristics using small and incomplete data from surveys about behavior episodes. We explored the prediction quality of the models in case of rare behavior. The test dataset was automatically generated and included 24465 cases. During the experiment, we considered cases with different rates to compare prediction quality. Our findings suggest that the model had a good prediction quality especially for rare and frequent behaviors (about 92% accuracy) and lower measures for medium-rate behaviors (about 86% accuracy).

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Alena Suvorova
AU  - Alexander Tulupyev
PY  - 2019/08
DA  - 2019/08
TI  - Prediction quality of Bayesian belief network model for risky behavior: comparison of subsamples with different rates
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 648
EP  - 652
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.90
DO  - 10.2991/eusflat-19.2019.90
ID  - Suvorova2019/08
ER  -