Two Evolutionary Methods for Learning Bayesian Network Structures

Delaplace, Alain; Brouard, Thierry; Cardot, Hubert

doi:10.1007/978-3-540-74377-4_31

Cited by 13 publications

(18 citation statements)

References 11 publications

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“…For example, following on from the online algorithm of , Tian et al (2001) have a procedure (IEMA) that combines an evolutionary algorithm and the expectation-maximization procedure to learn structure in the context of hidden variables. Finally, Delaplace et al (2006) showcase a refined GA, which includes tabu search and a dynamic mutation rate. Morales et al (2004) use a fuzzy system that combines the values of different scoring criteria, while performing a GA search.…”

Section: Genetic and Evolutionary Algorithmsmentioning

confidence: 99%

Learning Bayesian networks: approaches and issues

Daly

Shen

Aitken

2011

The Knowledge Engineering Review

269

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: Genetic and Evolutionary Algorithmsmentioning

confidence: 99%

Learning Bayesian networks: approaches and issues

Daly

Shen

Aitken

2011

The Knowledge Engineering Review

269

194

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“…First we learn the structure of the network by genetic algorithms proposed by Delaplace et al [22]. These are evolutionary algorithms, but in our case they have provided stable results (for a given dataset multiple invocations always returned identical network structures).…”

Section: Learning Phasementioning

confidence: 99%

Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition

Luqman

Delalandre

Brouard

et al. 2010

Graphics Recognition. Achievements, Challenges, and Evolution

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Abstract. The motivation behind our work is to present a new methodology for symbol recognition. The proposed method employs a structural approach for representing visual associations in symbols and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph. We have addressed the sensitivity of structural representations to noise, by using data adapted fuzzy intervals. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set. The Bayesian network is deployed in a supervised learning scenario for recognizing query symbols. The method has been evaluated for robustness against degradations & deformations on pre-segmented 2D linear architectural & electronic symbols from GREC databases, and for its recognition abilities on symbols with context noise i.e. cropped symbols.

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“…We use a Bayesian network for symbol recognition. This network is learned from underlying training data by using the quite recently proposed genetic algorithms for Bayesian network learning by Delaplace et al [12]. A query symbol is classified by using Bayesian probabilistic inference (on encoded joint probability distribution).We have selected the features in signature very carefully to best suit them to linear graphic symbols and to restrict their number to minimum; as Bayesian network algorithms are known to perform better for a smaller number of nodes.…”

Section: Originality Of Our Approachmentioning

confidence: 99%

Graphic Symbol Recognition Using Graph Based Signature and Bayesian Network Classifier

Luqman

Brouard

Ramel

2009

2009 10th International Conference on Document Analysis and Recognition

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We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational graph, which is used for computing a feature vector for the symbol. This signature corresponds to geometry and topology of the symbol. We learn a Bayesian network to encode joint probability distribution of symbol signatures and use it in a supervised learning scenario for graphic symbol recognition. We have evaluated our method on synthetically deformed and degraded images of presegmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates.

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Two Evolutionary Methods for Learning Bayesian Network Structures

Cited by 13 publications

References 11 publications

Learning Bayesian networks: approaches and issues

Learning Bayesian networks: approaches and issues

Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition

Graphic Symbol Recognition Using Graph Based Signature and Bayesian Network Classifier

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