Learning tractable Bayesian networks in the space of elimination orders

Benjumeda, Marco; Bielza, Concha; Larrañaga, Pedro

doi:10.1016/j.artint.2018.11.007

Cited by 14 publications

(18 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, the BN community has some opensource software packages that are well maintained and have a good extensibility; however, they are designed for highly specific tasks, e.g. learning algorithms (Aragam et al, 2019; Benjumeda et al, 2019) and inference algorithms (Højsgaard, 2012). We also have other packages, such as bnlearn (Scutari, 2010) and pgmpy (Ankan and Panda, 2015), which comprise a set of interconnected tools but they lack some basic modules, e.g., a graphical interface or connection with other packages, which would make them to be considered as frameworks.…”

Section: Problems With State-of-the-art Of Software In Massive Bn Intmentioning

confidence: 99%

BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience

Michiels

Larrañaga

Bielza

2020

Preprint

Self Cite

View full text Add to dashboard Cite

AbstractBayeSuites is the first web framework for learning, visualizing, and interpreting Bayesian networks (BNs) that can scale to tens of thousands of nodes while providing fast and friendly user experience. All the necessary features that enable this are reviewed in this paper; these features include scalability, extensibility, interoperability, ease of use, and interpretability. Scalability is the key factor in learning and processing massive networks within reasonable time; for a maintainable software open to new functionalities, extensibility and interoperability are necessary. Ease of use and interpretability are fundamental aspects of model interpretation, fairly similar to the case of the recent explainable artificial intelligence trend. We present the capabilities of our proposed framework by highlighting a real example of a BN learned from genomic data obtained from Allen Institute for Brain Science. The extensibility properties of the software are also demonstrated with the help of our BN-based probabilistic clustering implementation, together with another genomic-data example.

show abstract

Section: Problems With State-of-the-art Of Software In Massive Bn Intmentioning

confidence: 99%

BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience

Michiels

Larrañaga

Bielza

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The time complexity of tractable SEM is highly sensitive to the computational cost of searching for low-width EOs. Benjumeda et al [55] demonstrated that elimination trees (ETs) [56] can be used to implement an efficient heuristic for this problem. In this section, we apply their approach to search for low-width EOs.…”

Section: Efficient Search Of Low-width Elimination Ordersmentioning

confidence: 99%

“…Circumventing this redundancy reduces the size of the search space during the learning process. Grant and Horsch [56] proposed elimination trees (ETs), a representation for recursive conditioning, which has been adapted to exploit equivalence among EOs [55] .…”

Section: Efficient Search Of Low-width Elimination Ordersmentioning

confidence: 99%

“…Moreover, there are situations where a single local change can produce a significant change in the width of the EO. The ET optimisation heuristic proposed in [55] is used to mitigate this problem. The method (optimise_ET in Algorithm 2 ) receives an ET E B as input and greedily searches for lower-width ETs until no improvement is found.…”

Section: Displays An Example Of a Set Of Eos S Represented By An Et E Bmentioning

confidence: 99%

See 1 more Smart Citation

Tractable learning of Bayesian networks from partially observed data

Benjumeda

Luengo-Sánchez

Larrañaga

et al. 2019

Pattern Recognition

Self Cite

View full text Add to dashboard Cite

The majority of real-world problems require addressing incomplete data. The use of the structural expectation-maximization algorithm is the most common approach toward learning Bayesian networks from incomplete datasets. However, its main limitation is its demanding computational cost, caused mainly by the need to make an inference at each iteration of the algorithm. In this paper, we propose a new method with the purpose of guaranteeing the efficiency of the learning process while improving the performance of the structural expectation-maximization algorithm. We address the first objective by applying an upper bound to the treewidth of the models to limit the complexity of the inference. To achieve this, we use an efficient heuristic to search the space of the elimination orders. For the second objective, we study the advantages of directly computing the score with respect to the observed data rather than an expectation of the score, and provide a strategy to efficiently perform these computations in the proposed method. We perform exhaustive experiments on synthetic and real-world datasets of varied dimensionalities, including datasets with thousands of variables and hundreds of thousands of instances. The experimental results support our claims empirically.

show abstract

“…To deal with this important problem, there are 2 main methods that are used to create classification methods for multi-class data: The traditional base model method and the ensemble model method. At the traditional base model method [30][31][32][33][34][35][36][37][38][39][40], the model used to classify the resultant class, such as classifying the resultant class The nature of the decision tree, which is the decision tree method [41], is used to predict the pattern recognition class of the individual base model. [5,[42][43][44] Accuracy depends on the factors of the prediction of the result of class [45].…”

Section: Introductionmentioning

confidence: 99%

Cost-sensitive probability for weighted voting in an ensemble model for multi-class classification problems

Rojarath

Songpan

2021

Appl Intell

View full text Add to dashboard Cite

Ensemble learning is an algorithm that utilizes various types of classification models. This algorithm can enhance the prediction efficiency of component models. However, the efficiency of combining models typically depends on the diversity and accuracy of the predicted results of ensemble models. However, the problem of multi-class data is still encountered. In the proposed approach, cost-sensitive learning was implemented to evaluate the prediction accuracy for each class, which was used to construct a cost-sensitivity matrix of the true positive (TP) rate. This TP rate can be used as a weight value and combined with a probability value to drive ensemble learning for a specified class. We proposed an ensemble model, which was a type of heterogenous model, namely, a combination of various individual classification models (support vector machine, Bayes, K-nearest neighbour, naïve Bayes, decision tree, and multi-layer perceptron) in experiments on 3-, 4-, 5- and 6-classifier models. The efficiencies of the propose models were compared to those of the individual classifier model and homogenous models (Adaboost, bagging, stacking, voting, random forest, and random subspaces) with various multi-class data sets. The experimental results demonstrate that the cost-sensitive probability for the weighted voting ensemble model that was derived from 3 models provided the most accurate results for the dataset in multi-class prediction. The objective of this study was to increase the efficiency of predicting classification results in multi-class classification tasks and to improve the classification results.

show abstract

Learning tractable Bayesian networks in the space of elimination orders

Cited by 14 publications

References 40 publications

BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience

BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience

Tractable learning of Bayesian networks from partially observed data

Cost-sensitive probability for weighted voting in an ensemble model for multi-class classification problems

Contact Info

Product

Resources

About