scholarly journals Data-Driven Bayesian Network Learning: A Bi-Objective Approach to Address the Bias-Variance Decomposition

2020 ◽  
Vol 25 (2) ◽  
pp. 37 ◽  
Author(s):  
Vicente-Josué Aguilera-Rueda ◽  
Nicandro Cruz-Ramírez ◽  
Efrén Mezura-Montes
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Variance Decomposition ◽  
Data Driven ◽  
End User ◽  
Learning Problem ◽  
Nsga Ii ◽  
Bias Variance ◽  
Core Idea ◽  
Nondominated Sorting

We present a novel bi-objective approach to address the data-driven learning problem of Bayesian networks. Both the log-likelihood and the complexity of each candidate Bayesian network are considered as objectives to be optimized by our proposed algorithm named Nondominated Sorting Genetic Algorithm for learning Bayesian networks (NS2BN) which is based on the well-known NSGA-II algorithm. The core idea is to reduce the implicit selection bias-variance decomposition while identifying a set of competitive models using both objectives. Numerical results suggest that, in stark contrast to the single-objective approach, our bi-objective approach is useful to find competitive Bayesian networks especially in the complexity. Furthermore, our approach presents the end user with a set of solutions by showing different Bayesian network and their respective MDL and classification accuracy results.

Bias-variance decomposition of errors in data-driven land cover change modeling

2016 ◽  
Vol 31 (10) ◽  
pp. 2397-2413 ◽  
Author(s):  
Jing Gao ◽  
Amy C. Burnicki ◽  
James E. Burt
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Variance Decomposition ◽  
Data Driven ◽  
Bias Variance

Application of Bayesian Network in Safety Evaluation of Metro Construction

2013 ◽  
Vol 838-841 ◽  
pp. 1463-1468
Author(s):  
Xiang Ke Liu ◽  
Zhi Shen Wang ◽  
Hai Liang Wang ◽  
Jun Tao Wang
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Posterior Probability ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Safety Evaluation ◽  
Tree Analysis ◽  
Tunnel Blasting ◽  
Better Than

The paper introduced the Bayesian networks briefly and discussed the algorithm of transforming fault tree into Bayesian networks at first, then regarded the structures impaired caused by tunnel blasting construction as a example, introduced the built and calculated method of the Bayesian networks by matlab. Then assumed the probabilities of essential events, calculated the probability of top event and the posterior probability of each essential events by the Bayesian networks. After that the paper contrast the characteristics of fault tree analysis and the Bayesian networks, Identified that the Bayesian networks is better than fault tree analysis in safety evaluation in some case, and provided a valid way to assess risk in metro construction.

scholarly journals Modeling the City Distribution System Reliability with Bayesian Networks to Identify Influence Factors

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hao Zhang ◽  
Liyu Zhu ◽  
Shensi Xu
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
System Reliability ◽  
Distribution System ◽  
Complex Problem ◽  
Practical Significance ◽  
Correlation Degree ◽  
Urban Distribution ◽  
Distribution System Reliability ◽  
The City

Under the increasingly uncertain economic environment, the research on the reliability of urban distribution system has great practical significance for the integration of logistics and supply chain resources. This paper summarizes the factors that affect the city logistics distribution system. Starting from the research of factors that influence the reliability of city distribution system, further construction of city distribution system reliability influence model is built based on Bayesian networks. The complex problem is simplified by using the sub-Bayesian network, and an example is analyzed. In the calculation process, we combined the traditional Bayesian algorithm and the Expectation Maximization (EM) algorithm, which made the Bayesian model able to lay a more accurate foundation. The results show that the Bayesian network can accurately reflect the dynamic relationship among the factors affecting the reliability of urban distribution system. Moreover, by changing the prior probability of the node of the cause, the correlation degree between the variables that affect the successful distribution can be calculated. The results have significant practical significance on improving the quality of distribution, the level of distribution, and the efficiency of enterprises.

Shape optimization of egg-shaped sewer pipes based on the nondominated sorting genetic algorithm (NSGA-II)

2022 ◽  
Vol 204 ◽  
pp. 111999
Author(s):  
Hanting Wu ◽  
Yangrui Huang ◽  
Lei Chen ◽  
Yingjie Zhu ◽  
Huaizheng Li
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Nsga Ii ◽  
Sewer Pipes ◽  
Nondominated Sorting

scholarly journals Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Electricity Market ◽  
Optimization Methods ◽  
Evolutionary Programming ◽  
Congestion Management ◽  
Transmission Congestion ◽  
Nsga Ii ◽  
Line Overload ◽  
Nondominated Sorting

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

scholarly journals A dynamic Bayesian network approach for prognosis computations on discrete state systems

2017 ◽  
Vol 231 (5) ◽  
pp. 516-533 ◽  
Author(s):  
Josquin Foulliaron ◽  
Laurent Bouillaut ◽  
Patrice Aknin ◽  
Anne Barros
Keyword(s):  
Complex Systems ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Multicomponent System ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Discrete State ◽  
Virtual Maintenance

The maintenance optimization of complex systems is a key question. One important objective is to be able to anticipate future maintenance actions required to optimize the logistic and future investments. That is why, over the past few years, the predictive maintenance approaches have been an expanding area of research. They rely on the concept of prognosis. Many papers have shown how dynamic Bayesian networks can be relevant to represent multicomponent complex systems and carry out reliability studies. The diagnosis and maintenance group from French institute of science and technology for transport, development and networks (IFSTTAR) developed a model (VirMaLab: Virtual Maintenance Laboratory) based on dynamic Bayesian networks in order to model a multicomponent system with its degradation dynamic and its diagnosis and maintenance processes. Its main purpose is to model a maintenance policy to be able to optimize the maintenance parameters due to the use of dynamic Bayesian networks. A discrete state-space system is considered, periodically observable through a diagnosis process. Such systems are common in railway or road infrastructure fields. This article presents a prognosis algorithm whose purpose is to compute the remaining useful life of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a dynamic Bayesian network in order to be next integrated into the Virtual Maintenance Laboratory model to include the set of predictive maintenance policies. Inference computation questions on the considered dynamic Bayesian networks will be discussed. Finally, an application on simulated data will be presented.

Application of Bayesian for the Situation Assessment of Sea-Battlefield

2013 ◽  
Vol 346 ◽  
pp. 135-139 ◽  
Author(s):  
Yong Tao Yu ◽  
Ying Ding ◽  
Zheng Xi Ding
Keyword(s):  
Decision Support ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Assessment Model ◽  
Situation Assessment ◽  
Network Characteristics ◽  
Specific Assessment

The sea-battlefield situation is dynamic and how efficient sea-battlefield situation assessment is a major problem facing operational decision support. According to research based on Bayesian networks Sea-battlefield situation assessment, first constructed sea-battlefield situation assessment Bayesian network; followed by specific assessment objectives, to simplify creating sub Bayesian assessment model; once again based on Bayesian network characteristics to determine each node probability formula; finally, according to the formula for solving the edge of the probability and the conditional probability of each node, sea-battlefield situation assessment.

scholarly journals The engineering skills training process modeling using dynamic bayesian nets

2021 ◽  
pp. 87-96
Author(s):  
Andrey Chukhray ◽  
Olena Havrylenko
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Learning Process ◽  
Probabilistic Inference ◽  
Dynamic Bayesian Network ◽  
Computer Training ◽  
Skills Training ◽  
Dynamic Bayesian Networks ◽  
Competence Level ◽  
Intelligent Computer

The subject of research in the article is the process of intelligent computer training in engineering skills. The aim is to model the process of teaching engineering skills in intelligent computer training programs through dynamic Bayesian networks. Objectives: To propose an approach to modeling the process of teaching engineering skills. To assess the student competence level by considering the algorithms development skills in engineering tasks and the algorithms implementation ability. To create a dynamic Bayesian network structure for the learning process. To select values for conditional probability tables. To solve the problems of filtering, forecasting, and retrospective analysis. To simulate the developed dynamic Bayesian network using a special Genie 2.0-environment. The methods used are probability theory and inference methods in Bayesian networks. The following results are obtained: the development of a dynamic Bayesian network for the educational process based on the solution of engineering problems is presented. Mathematical calculations for probabilistic inference problems such as filtering, forecasting, and smoothing are considered. The solution of the filtering problem makes it possible to assess the current level of the student's competence after obtaining the latest probabilities of the development of the algorithm and its numerical calculations of the task. The probability distribution of the learning process model is predicted. The number of additional iterations required to achieve the required competence level was estimated. The retrospective analysis allows getting a smoothed assessment of the competence level, which was obtained after the task's previous instance completion and after the computation of new additional probabilities characterizing the two checkpoints implementation. The solution of the described probabilistic inference problems makes it possible to provide correct information about the learning process for intelligent computer training systems. It helps to get proper feedback and to track the student's competence level. The developed technique of the kernel of probabilistic inference can be used as the decision-making model basis for an automated training process. The scientific novelty lies in the fact that dynamic Bayesian networks are applied to a new class of problems related to the simulation of engineering skills training in the process of performing algorithmic tasks.

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