Distributed Particle Swarm Optimization for Structural Bayesian Network Learning

Sahin, Ferat; Devasia, Archana

doi:10.5772/5120

Cited by 13 publications

(8 citation statements)

References 33 publications

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“…Xing- Chen et al (2007a) and Heng et al (2006) have applied this in the case of normal Bayesian networks and also in the case of DBNs Xing- Chen et al (2007b). Other approaches include those by Li et al (2006), who use a memory binary particle swarm optimization technique and by Sahin and Devasia (2007) who use a distributed particle swarm optimization approach.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Learning Bayesian networks: approaches and issues

Daly

Shen

Aitken

2011

The Knowledge Engineering Review

271

194

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Daly, R., Shen, Q., Aitken, S. (2011). Learning Bayesian networks: approaches and issues. Knowledge Engineering Review, 26 (2), 99-157Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to the difficulty domain experts have in specifying them, techniques that learn Bayesian networks from data have become indispensable. Recently, however, there have been many important new developments in this field. This work takes a broad look at the literature on learning Bayesian networks?in particular their structure?from data. Specific topics are not focused on in detail, but it is hoped that all the major fields in the area are covered. This article is not intended to be a tutorial?for this, there are many books on the topic, which will be presented. However, an effort has been made to locate all the relevant publications, so that this paper can be used as a ready reference to find the works on particular sub-topics.Peer reviewe

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Learning Bayesian networks: approaches and issues

Daly

Shen

Aitken

2011

The Knowledge Engineering Review

271

194

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“…Discrete search spaces are not differentiable, therefore it is not possible to use the usual definition of velocity and inertia for them. The approach proposed by Binary PSO, used by [16], changes the search space to a continuous multi-dimensional space where each variable may be on the interval [0, 1], and this value is the likelihood of a certain discrete feature on the search space. On the other side, some authors try to adapt the operations done with position and velocity according to the specific solution domain [14], [15].…”

Section: Introductionmentioning

confidence: 99%

“…Authors have adopted two main approaches for adapting Particle Swarm Optimization for the problem of learning a Bayesian network structure: one is directly adapt the concepts of PSO to the network structure learning problem, such as [14] and [15]; and the other is to use more general-purpose discrete adaptations of PSO, such as Binary PSO [16], where there is a translation of network representations to the representations required in these PSO methods. The main question behind these different approaches is the interpretation of the concepts of velocity and movement of particles in a discrete search space.…”

Section: Introductionmentioning

confidence: 99%

A PSO approach for learning transition structures of Higher-Order Dynamic Bayesian Networks

Santos

Maciel

2014

5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC)

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Dynamic Bayesian Networks are widely used for modeling neural information flow and gene regulatory networks.Their assumption of first-order Markov, however, is recently being noted as a too restrictive assumption for some applications, specially when these have communication and processing of information at different time delays. Many authors are extending this Markov assumption to higher orders, suggesting the use of Higher-Order Dynamic Bayesian Networks (HO-DBNs). These networks, by their turn, bring some issues to be considered, mainly because of the elevated number of nodes, the necessity of great amounts of data and the high cost for learning their structures. In this work we propose an optimization technique based on Particle Swarm Optimization for learning the transition structures of HO-DBNs, trying to exploit their intrinsic characteristics to suggest efficient representations and simplifications in their learning. Also, we propose a way to introduce extra randomness in the method by reinterpreting the concepts of inertia and social/cognitive contributions to particles. We test the algorithm for correctness and performance, comparing it against a greedy algorithm for learning Bayesian structures.

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“…Nas áreas de aprendizagem estrutural de redes Bayesianas baseada em heurísticas, recentemente vários métodos de otimização têm sido adaptados -como os evolutivos (Villanueva e Maciel, 2010, baseados em simulated annealing (Ye et al, 2008), otimização por colônia de formigas (ACO) (Daly e Shen, 2009) e por enxame de partículas (PSO) (Sahin e Devasia, 2007). No entanto, em todos estes casos as heurísticas são utilizadas para a otimização de uma função de pontuação, que utiliza diretamente os dados fornecidos para o treinamento, e não medidas de transferência de informação como informação mútua e transferência de entropia.…”

Section: Aprendizado Estrutural Greedyunclassified

Identicação de sistemas neurais com redes bayesianas dinâmicas e transferência de entropia

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Distributed Particle Swarm Optimization for Structural Bayesian Network Learning

Cited by 13 publications

References 33 publications

Learning Bayesian networks: approaches and issues

Learning Bayesian networks: approaches and issues

A PSO approach for learning transition structures of Higher-Order Dynamic Bayesian Networks

Identicação de sistemas neurais com redes bayesianas dinâmicas e transferência de entropia

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