Learning Bayesian networks from data: An information-theory based approach

Cheng, Jie; Greiner, Russell; Kelly, Jonathan; Bell, David A.; Liu, Weiru

doi:10.1016/s0004-3702(02)00191-1

Cited by 728 publications

(530 citation statements)

References 27 publications

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“…We can make a comparison 3 between the two steps method proposed and the single searching process. For that purpose we have used a constraint based algorithm, BN Power Constructor (BNPC) [11] (we use the software package available at http://www.cs.ualberta.ca/˜jcheng /bnsoft.htm ). In Table 2 we show the results obtained by the BNPC algorithm which are worse than those obtained by any of the different orderings θ, used as entry to the algorithms BENEDICT-step and K2.…”

Section: Resultsmentioning

confidence: 99%

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The Search of Causal Orderings: A Short Cut for Learning Belief Networks

Acid

Campos

Huete

2001

Lecture Notes in Computer Science

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Abstract. Although we can build a belief network starting from any ordering of its variables, its structure depends heavily on the ordering being selected: the topology of the network, and therefore the number of conditional independence relationships that may be explicitly represented can vary greatly from one ordering to another. We develop an algorithm for learning belief networks composed of two main subprocesses: (a) an algorithm that estimates a causal ordering and (b) an algorithm for learning a belief network given the previous ordering, each one working over different search spaces, the ordering and dag space respectively.

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Section: Resultsmentioning

confidence: 99%

“…The number and complexity of the tests are critical for the efficiency and reliability of these methods. Some of the algorithms based on this approach can be found in [10,11,16].…”

Section: Learning Belief Networkmentioning

confidence: 99%

The Search of Causal Orderings: A Short Cut for Learning Belief Networks

Acid

Campos

Huete

2001

Lecture Notes in Computer Science

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“…Comparing to classical statistical approaches, Bayesian belief networks have a distinct advantage [15]. BBN becomes not only a powerful tool for knowledge representation but reasoning under conditions of uncertainty [16], frequently dealing with real-world problems such as building medical diagnostic systems, forecasting, and manufacturing process control for several decades [17]. Nowadays BBN has been extended to other applications including software risk management [18], ecosystem and environmental management [19], and transportation [20].…”

Section: Bayesian Belief Networkmentioning

confidence: 99%

Airlines Content Recommendations Based on Passengers' Choice Using Bayesian Belief Networks

Chen¹,

Huang²,

Chen³

et al. 2017

Bayesian Inference

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Faced with the increasingly fierce competition in the aviation market, the strategy of consumer choice has gained increasing significance in both academia and practice. As ever-increasing travel choices and growing consumer heterogeneity, how do airline companies satisfy passengers' needs? With a vast amount of data, how do airline managers combine information to excavate the relationship between independent variables to gain insight about passengers' choices and value system as well as determining best personalized contents to them? Using the real case of China Southern Airlines, this paper illustrates how Bayesian belief network (BBN) can enable airlines dynamically recommend relevant contents based on predicting passengers' choice to optimize the loyalty. The findings of this study provide airline companies useful insights to better understand the passengers' choices and develop effective strategies for growing customer relationship.

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“…In a comparative study by [23], authors identified some currently used structure learning algorithms, namely PC [60] or IC/IC * [50] (causality search using statistical tests to evaluate conditional independence), BN Power Constructor (BNPC) [11] (also uses conditional independence tests) and other methods based on scoring criterion, such as Minimal weight spanning tree (MWST) [16] (intelligent weighting of the edges and application of the well-known algorithms for the problem of the minimal weight tree), K2 [18] (maximisation of P(G|D) using Bayes and a topological order on the nodes), Greedy search [12] (finding the best neighbour and iterate) or SEM [24] (extension of the EM meta-algorithm to the structure learning problem). However that may be, the problem of learning an optimal Bayesian network from a given dataset is NP-hard [13].…”

Section: Parameter and Structure Learningmentioning

confidence: 99%

Cooperative Coevolution for Agrifood Process Modeling

Barrière

Lutton

Wuillemin

et al. 2013

EVOLVE- A Bridge Between Probability, Set Oriented Numerics and Evolutionary Computation

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On the contrary to classical schemes of evolutionary optimisations algorithms, single population Cooperative Co-evolution techniques (CCEAs, also called "Parisian" approaches) make it possible to represent the evolved solution as an aggregation of several individuals (or even as a whole population). In other words, each individual represents only a part of the solution. This scheme allows simulating the principles of Darwinian evolution in a more economic way, which results in gain in robustness and efficiency. The counterpart however is a more complex design phase. In this chapter, we detail the design of efficient CCEAs schemes on two applications related to the modeling of an industrial agri-food process. The experiments correspond to complex optimisations encountered in the modeling of a Camembert-cheese ripening process. Two problems are considered:• A deterministic modeling problem, phase prediction, for which a search for a closed form tree expression is performed using genetic programming (GP).• A Bayesian network structure estimation problem. The novelty of the proposed approach is based on the use of a two step process based on an intermediate representation called independence model. The search for an independence model is formulated as a complex optimisation problem, for which the CCEA scheme is particularly well suited. A Bayesian network is finally deduced using a deterministic algorithm, as a representative of the equivalence class figured by the independence model.

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Learning Bayesian networks from data: An information-theory based approach

Cited by 728 publications

References 27 publications

The Search of Causal Orderings: A Short Cut for Learning Belief Networks

The Search of Causal Orderings: A Short Cut for Learning Belief Networks

Airlines Content Recommendations Based on Passengers' Choice Using Bayesian Belief Networks

Cooperative Coevolution for Agrifood Process Modeling

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