Discretizing environmental data for learning Bayesian-network classifiers

Ropero, Rosa María Fernández; Renooij, Silja; Gaag, Linda C. van der

doi:10.1016/j.ecolmodel.2017.12.015

Cited by 39 publications

(15 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…State variables in natural systems are most often continuous, and discretization of continuous variables, may result in information loss (Uusitalo, 2007 ). The method employed for discretization may impact on the predictive quality of the model (Ropero, Renooji, & van der Gaag, 2018 ), and careful consideration must be given to when and how to discretize. For data‐driven BNs without a priori distributions, a large number of intervals for a variable can result in zero frequencies and a failure to be adequately representative of the natural system (Uusitalo, 2007 ).…”

Section: Challenges In Bayesian Network Modeling Of South African Wat...mentioning

confidence: 99%

Bayesian Network Applications for Sustainable Holistic Water Resources Management: Modeling Opportunities for South Africa

2021

View full text Add to dashboard Cite

Anthropogenic transformation of land globally is threatening water resources in terms of quality and availability. Managing water resources to ensure sustainable utilization is important for a semiarid country such as South Africa. Bayesian networks (BNs) are probabilistic graphical models that have been applied globally to a range of water resources management studies; however, there has been very limited application of BNs to similar studies in South Africa. This article explores the benefits and challenges of BN application in the context of water resources management, specifically in relation to South Africa. A brief overview describes BNs, followed by details of some of the possible opportunities for BNs to benefit water resources management. These include the ability to use quantitative and qualitative information, data, and expert knowledge. BN models can be integrated into geographic information systems and predict impact of ecosystem services and sustainability indicators. With additional data and information, BNs can be updated, allowing for integration into an adaptive management process. Challenges in the application of BNs include oversimplification of complex systems, constraints of BNs with categorical nodes for continuous variables, unclear use of expert knowledge, and treatment of uncertainty. BNs have tremendous potential to guide decision making by providing a holistic approach to water resources management.

show abstract

Section: Challenges In Bayesian Network Modeling Of South African Wat...mentioning

confidence: 99%

Bayesian Network Applications for Sustainable Holistic Water Resources Management: Modeling Opportunities for South Africa

2021

View full text Add to dashboard Cite

show abstract

“…The naive Bayesian classifier is a classifier based on the Bayes’ theorem. 34,35 For a sample { x 1 , x 2 ,⋯ , x n } , the probability of this sample belonging to label c can be expressed by Bayes’ theorem as…”

Section: The Tan Classifier and Its Adaboost Algorithmmentioning

confidence: 99%

An AdaBoost-based tree augmented naive Bayesian classifier for transient stability assessment of power systems

Wang

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

By focusing on the accuracy limitations of the naive Bayesian classifier in the transient stability assessment of power systems, a tree augmented naive Bayesian (TAN) classifier is adopted for the power system transient stability assessment. The adaptive Boosting (AdaBoost) algorithm is used in the TAN classifier to form an AdaBoost-based tree augmented naive Bayesian (ATAN) classifier for further classification performance improvement. To construct the ATAN classifier, eight attributes that reasonably reflect the transient stability or transient instability of a power system are selected as inputs of the proposed classifier. In addition, the class-attribute interdependence maximization (CAIM) algorithm is used to discretize the attributes. Then, the operating mechanism of the power system is used to obtain the dependencies between the attributes, and the parameters of the ATAN classifier are learned according to the Bayes’ theorem and the criterion of maximizing a posterior estimation. Four evaluation indicators of the ATAN classifier are used, that is, the value of Kappa, the area under the receiver operating characteristic curve (AUC), F1 score, and the average evaluation indicator. Lastly, experiments are implemented on the IEEE 3-generator 9-bus system and IEEE 10-generator 39-bus system. The simulation results show that the ATAN classifier can significantly improve the classification performance of the transient stability assessment of the power system.

show abstract

“…The variables are discretized into three levels using the equal frequency method (see e.g. Ropero et al, 2018). A BN over these variables together with the binary variable Muscle Loss (ML) is learnt using an hill-climbing algorithm and reported in Figure 6 (left).…”

Section: Cancer-associated Muscle Wastingmentioning

confidence: 99%

Staged trees and asymmetry-labeled DAGs

Varando¹,

Carli²,

Leonelli³

2021

Preprint

View full text Add to dashboard Cite

Bayesian networks are a widely-used class of probabilistic graphical models capable of representing symmetric conditional independence between variables of interest using the topology of the underlying graph. They can be seen as a special case of the much more general class of models called staged trees, which can represent any type of non-symmetric conditional independence. Here we formalize the relationship between these two models and introduce a minimal Bayesian network representation of the staged tree, which can be used to read conditional independences in an intutitive way. Furthermore, we define a new labeled graph, termed asymmetry-labeled directed acyclic graph, whose edges are labeled to denote the type of dependence existing between any two random variables. Various datasets are used to illustrate the methodology, highlighting the need to construct models which more flexibly encode and represent non-symmetric structures.

show abstract

Discretizing environmental data for learning Bayesian-network classifiers

Cited by 39 publications

References 42 publications

Bayesian Network Applications for Sustainable Holistic Water Resources Management: Modeling Opportunities for South Africa

Bayesian Network Applications for Sustainable Holistic Water Resources Management: Modeling Opportunities for South Africa

An AdaBoost-based tree augmented naive Bayesian classifier for transient stability assessment of power systems

Staged trees and asymmetry-labeled DAGs

Contact Info

Product

Resources

About