Bayesian Belief Networks in Risky Behavior Modelling

Суворова, Алена Владимировна; Tulupyeva, Tatiana V.

doi:10.1007/978-3-319-33816-3_10

Cited by 3 publications

(2 citation statements)

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“…The input data for the model [17] include the lengths of intervals between three last episodes of risky behavior and the lengths of minimum and maximum intervals between episodes during a given period of interest. The data about episodes in most applications is obtained from respondents self-reports [18]. In addition, the model includes the latent variable n that corresponds to the number of episodes during the period of interest and the behavior rate, that is the key variable, the one we want to estimate.…”

Section: Introductionmentioning

confidence: 99%

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

Суворова

Tulupyev

2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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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).

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Section: Introductionmentioning

confidence: 99%

“…Further studies [18] explored other structures including those that were data-based and used structure- Figure 2). In this structure the behavior rate is related to number of episodes and all other variables depended on the number of episodes only.…”

Section: Introductionmentioning

confidence: 99%