Inference and Learning in Multi-dimensional Bayesian Network Classifiers

Waal, Peter R. de; Gaag, Linda C. van der

doi:10.1007/978-3-540-75256-1_45

Cited by 36 publications

(29 citation statements)

References 7 publications

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“…Regard on MBCs, the tree-structure for class and feature subgraphs are learnt in [2]. In [3], a method is proposed for the recovery of poly-trees structures in both subgraphs. In [13], Kdependency Bayesian (KDB) multi-dimensional classifiers are built, which can be viewed as the extensions of the polytrees structures.…”

Section: Related Workmentioning

confidence: 99%

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Approaching Multi-dimensional Classification by Using Bayesian Network Chain Classifiers

Zhang

Yang

Zhu

2014

2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics

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Multi-dimensional classification assigns an unseen instance to more than one class variable simultaneously. Bayesian chain classifiers have been recently proposed to address the task since they were first proposed in 2011. However, when the simplistic structure of Bayesian network is built to represent the dependency relationships among classes, the predictive performance will degrade somewhat. In this paper, we further study Bayesian chain classifiers in more explicit dependency structures for class variables by using score-based methods, that is, we learn general structures of Bayesian network by using the K2 and HillClimbing algorithms to represent the dependency relationships among classes. Meanwhile, we employ a fast clustering-based feature subset selection method for constructing selective Naïve Bayesian network classifiers, as base classifiers. Ultimately, we develop the alternative Bayesian chain classifiers. Experimental results show that our models can be able to achieve better or at least comparable performance compared against other stateof-the-art methods, both in term of predictive performance and time complexity.

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Section: Related Workmentioning

confidence: 99%

“…Multi-dimensional Bayesian network classifiers (MBCs) have been viewed as useful tools for solving MDC [2], [3], [4]. However, the problem is that the super-exponential surge in computational complexity of the Bayesian network structure learning with the increase of variables.…”

Section: Introductionmentioning

confidence: 99%

Approaching Multi-dimensional Classification by Using Bayesian Network Chain Classifiers

Zhang

Yang

Zhu

2014

2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics

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“…A multi-objective EDA based on this idea is MBN-EDA [14], which uses multidimensional Bayesian networks (MBNs), a type of Bayesian networks [40] initially used in multi-dimensional classification [41,42], for joint modeling of variables and objectives. Figure 2 shows an example of an MBN structure.…”

Section: Multi-objective Optimization With Joint Variable-objective Pmentioning

confidence: 99%

Interval-based ranking in noisy evolutionary multi-objective optimization

2014

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As one of the most competitive approaches to multi-objective optimization, evolutionary algorithms have been shown to obtain very good results for many realworld multi-objective problems. One of the issues that can affect the performance of these algorithms is the uncertainty in the quality of the solutions which is usually represented with the noise in the objective values. Therefore, handling noisy objectives in evolutionary multi-objective optimization algorithms becomes very important and is gaining more attention in recent years. In this paper we present α-degree Pareto dominance relation for ordering the solutions in multi-objective optimization when the values of the objective functions are given as intervals. Based on this dominance relation, we propose an adaptation of the non-dominated sorting algorithm for ranking the solutions. This ranking method is then used in a standard multi-objective evolution ary algorithm and a recently proposed novel multi-objective estimation of distribution algorithm based on joint variable-objective probabilistic modeling, and applied to a set of multi-objective problems with different levels of independent noise. The experi mental results show that the use of the proposed method for solution ranking allows to approximate Pareto sets which are considerably better than those obtained when using the dominance probability-based ranking method, which is one of the main methods for noise handling in multi-objective optimization.

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“…As possible future research, we would like to extend our results to general multi-dimensional BN classifiers [4,12,2,13]. Multi-dimensional BN classifiers permit BN structures between classes and predictors, and so the multi-valued decision functions have to be found by a global maximum search over the possible class values.…”

Section: Consider the Bi-valued Decision Function F G T With The Formmentioning

confidence: 99%

Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification

Varando

Bielza

Larrañaga

2014

Probabilistic Graphical Models

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Abstract. Bayesian network classifiers are widely used in machine learning because they intuitively represent causal relations. Multi-label classification problems require each instance to be assigned a subset of a defined set of h labels. This problem is equivalent to finding a multivalued decision function that predicts a vector of h binary classes. In this paper we obtain the decision boundaries of two widely used Bayesian network approaches for building multi-label classifiers: Multi-label Bayesian network classifiers built using the binary relevance method and Bayesian network chain classifiers. We extend our previous single-label results to multi-label chain classifiers, and we prove that, as expected, chain classifiers provide a more expressive model than the binary relevance method.

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Inference and Learning in Multi-dimensional Bayesian Network Classifiers

Cited by 36 publications

References 7 publications

Approaching Multi-dimensional Classification by Using Bayesian Network Chain Classifiers

Approaching Multi-dimensional Classification by Using Bayesian Network Chain Classifiers

Interval-based ranking in noisy evolutionary multi-objective optimization

Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification

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