Structure space of Bayesian networks is dramatically reduced by subdividing it in sub-networks

Bouhamed, Heni; Masmoudi, Afif; Lecroq, Thierry; Rebaï, Ahmed

doi:10.1016/j.cam.2015.02.055

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…[2][3][4][5] A very high number of BNs can be modeled given a set of variables, and machine-learning methods have been successfully employed to learn the structure of BNs in an automated fashion. As Bouhamed et al 6 state, ''Currently, Bayesian Networks have become one of the most complete, self-sustained and coherent formalisms used for knowledge acquisition, representation and application through computer systems.'' Machine learning is a collection of methods for systems that can learn and automatically improve with experience.…”

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confidence: 99%

Comparison of Logistic Regression and Bayesian Networks for Risk Prediction of Breast Cancer Recurrence

et al. 2018

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confidence: 99%

Comparison of Logistic Regression and Bayesian Networks for Risk Prediction of Breast Cancer Recurrence

et al. 2018

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“…(6). Then, the approximate process of numerical solution about the differential-equation φ = B * y/D + A * φ is conducted in step (3) to step (21).…”

Section: Algorithm 3 S-mining ( a B D)mentioning

confidence: 99%

“…(iii) E-commerce data mining: Jamison and Snow studied online data and further proposed a structural equation model [15]; Yang studied the data stability model using the mobile industry as an example [16]; Thomas et al proposed an evaluation model to mine the e-commerce environment stable mode [17]. (iv) Research on network data stability: For the suddenness, randomness and ambiguity of data, Iova et al studied the stability of the network in a big data environment and proposed a verification model for network stability [18]; Zhang et al summarized methods to improve the stability of the network [19]; Chen et al studied the global exponential stability based on Hopfield neural networks and used it for optimization calculations [20]; Bouhamed et al studied the network structure and analyzed the network instability [21]. These studies will be an effective attempt to mine sensor data.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Data Mining of Sensor Data

2020

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The research of data mining has aroused widespread concern in academia and industry. However, an important mark of the Internet of Things era is that sensor data replaces artificially compiled data. How to extract valuable knowledge and patterns from a large amount of data generated by sensors is a meaningful research topic. This paper proposes a dynamic data mining framework for processing sensor data. A sensor data mining model which can be used in the process of dynamic change is constructed. In this model, different sensor network environments are considered as different physical systems. The physical system and its parameters are trained by collecting and mining historical changes in sensor data; the associations between different sensor network environments are discovered by mining the associations between the parameters of different physical systems. In our limited experimental environment, the physical quantities considered included transmission distance, transmission delay, sensor data, data changes, and so on. Experiments were carried out on the designated experimental platform, and the results showed that the model could mine the dynamic data and find stable patterns. Through the analysis of the experimental results, it was found that the model had reference value for the dynamic mining of sensor data, and was expected to construct new methods for industrial big data analysis. INDEX TERMS Clustering, dynamic characteristics, dynamic data mining, IoT, sensors.

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“…When the system being modelled presents a high degree of uncertainty and complexity, such as in ecosystems and environmental management, Bayesian Networks (BN) have become an increasingly popular modelling technique for risk assessment (Fenton and Neil, 2008), in different research fields, such as for the estimation of microbial risks for different end-uses of recycled water (Beaudequin et al, 2015), or sources of salinity in soil irrigated with recycled water (Rahman et al, 2015), or also the performance of manufacturing processes (Nannapaneni et al, 2016). The importance and usefulness of BN have been acknowledged in different fields such as artificial intelligence (Darwiche, 2009) and probability calculus (Conrady and Jouffe, 2015), and in general, BN have been recognised as "one of the most complete, self-sustained and coherent formalisms used for knowledge acquisition, representation and application through computer systems" (Bouhamed, 2015).…”

Section: Modelling Environmental Systems Under High Uncertaintymentioning

confidence: 99%

Extreme events, water quality and health: A participatory Bayesian risk assessment tool for managers of reservoirs

Bertone

Sahin

Richards

et al. 2016

Journal of Cleaner Production

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Extreme weather events pose major challenges for the delivery of safe drinking water, especially in a country like Australia. As a consequence, a participatory Bayesian Network modelling approach was used to develop a risk assessment tool for estimating, and ranking, water quality-related health risks associated with extreme weather events. The model was developed for a large dam supplying a water treatment plant in New South Wales, Australia. This methodological approach addresses challenges associated with fragmented data (for model parameterisation) and parameter uncertainty by eliciting and integrating quantitative and qualitative data (including expert opinions) into a single framework. Key-stakeholders were engaged in developing and then refining separate conceptual models around the three critical parameters of turbidity, water colour and Cryptosporidium sp. These three conceptual models were then combined into a single conceptual model, which then formed the basis for the Bayesian Network model. The final risk assessment tool was able to quantify the sensitivity of the water treatment plant's efficacy (ability to supply high quality potable water) in response to different extreme event scenarios. Overall, landslip-related events were the most concerning for water qualityrelated health risks, but an emergent outcome was how the scenarios were ranked quite differently depending on the group, and expertise of the stakeholders' opinions used to run the model. Such tool can assist stakeholders for an effective long-term water resource management.

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Structure space of Bayesian networks is dramatically reduced by subdividing it in sub-networks

Cited by 12 publications

References 13 publications

Comparison of Logistic Regression and Bayesian Networks for Risk Prediction of Breast Cancer Recurrence

Comparison of Logistic Regression and Bayesian Networks for Risk Prediction of Breast Cancer Recurrence

Dynamic Data Mining of Sensor Data

Extreme events, water quality and health: A participatory Bayesian risk assessment tool for managers of reservoirs

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