Scaling up the Greedy Equivalence Search algorithm by constraining the search space of equivalence classes

Alonso-Barba, Juan I.; delaOssa, Luis; Gámez, José A.; Puerta, José M.

doi:10.1016/j.ijar.2012.09.004

Cited by 34 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For isolation of engine fault classes with a data that has no labels assigned to, the unsupervised learning-based BBN might thus be preferable. Different algorithms exist for this purpose such as the score-based, constraint-based, and a hybrid of these two [194].…”

Section: Bayesian Belief Networkmentioning

confidence: 99%

A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

et al. 2019

View full text Add to dashboard Cite

Gas-path diagnostics is an essential part of gas turbine (GT) condition-based maintenance (CBM). There exists extensive literature on GT gas-path diagnostics and a variety of methods have been introduced. The fundamental limitations of the conventional methods such as the inability to deal with the nonlinear engine behavior, measurement uncertainty, simultaneous faults, and the limited number of sensors available remain the driving force for exploring more advanced techniques. This review aims to provide a critical survey of the existing literature produced in the area over the past few decades. In the first section, the issue of GT degradation is addressed, aiming to identify the type of physical faults that degrade a gas turbine performance, which gas-path faults contribute more significantly to the overall performance loss, and which specific components often encounter these faults. A brief overview is then given about the inconsistencies in the literature on gas-path diagnostics followed by a discussion of the various challenges against successful gas-path diagnostics and the major desirable characteristics that an advanced fault diagnostic technique should ideally possess. At this point, the available fault diagnostic methods are thoroughly reviewed, and their strengths and weaknesses summarized. Artificial intelligence (AI) based and hybrid diagnostic methods have received a great deal of attention due to their promising potentials to address the above-mentioned limitations along with providing accurate diagnostic results. Moreover, the available validation techniques that system developers used in the past to evaluate the performance of their proposed diagnostic algorithms are discussed. Finally, concluding remarks and recommendations for further investigations are provided.

show abstract

Section: Bayesian Belief Networkmentioning

confidence: 99%

A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

et al. 2019

View full text Add to dashboard Cite

show abstract

“…A hybrid method combines a score-based method either with a constraintbased method or with a variable selection method. Such methods often use a greedy search on a restricted search space in order to achieve computational efficiency, where the restricted space is estimated using conditional independence tests or variable selection methods [Tsamardinos et al, 2006;Schmidt et al, 2007;Schulte et al, 2010;Alonso-Barba et al, 2013]. Common choices for the restricted search space are an estimated skeleton of the CPDAG (CPDAG-skeleton) or an estimated conditional independence graph (CIG).…”

mentioning

confidence: 99%

“…The CIG is a supergraph of the CPDAG-skeleton. Hybrid algorithms generally scale well with respect to the number of variables, but their consistency results are generally lacking even in the classical setting, except for Alonso- Barba et al [2013].…”

mentioning

confidence: 99%

High-dimensional consistency in score-based and hybrid structure learning

Nandy¹,

Hauser²,

Maathuis³

2018

Ann. Statist.

107

View full text Add to dashboard Cite

Main approaches for learning Bayesian networks can be classified as constraint-based, score-based or hybrid methods. Although highdimensional consistency results are available for constraint-based methods like the PC algorithm, such results have not been proved for scorebased or hybrid methods, and most of the hybrid methods have not even shown to be consistent in the classical setting where the number of variables remains fixed and the sample size tends to infinity. In this paper, we show that consistency of hybrid methods based on greedy equivalence search (GES) can be achieved in the classical setting with adaptive restrictions on the search space that depend on the current state of the algorithm. Moreover, we prove consistency of GES and adaptively restricted GES (ARGES) in several sparse high-dimensional settings. ARGES scales well to sparse graphs with thousands of variables and our simulation study indicates that both GES and ARGES generally outperform the PC algorithm.

show abstract

“…For this purpose, we have generated a set of random BNs with different degrees of difficulty. 12 A summary of the properties of all these networks can be found in Table I. For each database, we have generated five samples of 5000 instances each.…”

Section: Methodsmentioning

confidence: 99%

“…Although these networks offer a high diversity with respect to dimensionality, ranging from 20 to 724 variables, we wished to extend our experiments by adding some big networks because solving high‐dimensional problems is the target of the scalable algorithms presented in this paper. For this purpose, we have generated a set of random BNs with different degrees of difficulty . A summary of the properties of all these networks can be found in Table .…”

Section: Experimental Evaluationmentioning

confidence: 99%

Structural Learning of Bayesian Networks Via Constrained Hill Climbing Algorithms: Adjusting Trade-off between Efficiency and Accuracy

Arias

Gámez

Puerta

2014

Int. J. Intell. Syst.

View full text Add to dashboard Cite

Learning Bayesian networks is known to be an NP‐hard problem, and this, combined with the growing interest in learning models from high‐dimensional domains, leads to the necessity of finding more efficient learning algorithms. Recent papers have proposed constrained approaches of successfully and widely used local search algorithms, such as Hill Climbing. One of these algorithms families, called constrained Hill Climbing (CHC), greatly improves upon the efficiency of the original approach, obtaining models with slightly lower quality but maintaining their theoretical properties. In this paper, we propose three different modifications to the most scalable version of these algorithms, fast constrained Hill Climbing, to improve the quality of its output by relaxing the constraints imposed to include some diversification in the search process. The aim of these new approaches is to adjust the trade‐off between efficiency and accuracy of the algorithm, as they do not modify its complexity and only imply a few more search iterations. We perform an intensive experimental evaluation of the modifications proposed with an extensive comparison between the original algorithms and the new modifications covering several scenarios with quite large data sets. Available code and data for further use of the algorithms presented in this paper and experiment replication can be available at http://simd.albacete.org/supplements/FastCHC.html.

show abstract

Scaling up the Greedy Equivalence Search algorithm by constraining the search space of equivalence classes

Cited by 34 publications

References 17 publications

A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

High-dimensional consistency in score-based and hybrid structure learning

Structural Learning of Bayesian Networks Via Constrained Hill Climbing Algorithms: Adjusting Trade-off between Efficiency and Accuracy

Contact Info

Product

Resources

About