Structure learning of Bayesian Networks using global optimization with applications in data classification

Taheri, Sona; Mammadov, Musa

doi:10.1007/s11590-014-0803-1

Cited by 12 publications

(5 citation statements)

References 30 publications

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“…We compared the proposed algorithm when

with several benchmark classifiers [ 12 , 13 , 23 ] that were presented in the literature. The statistical results of all evaluated functions using 20 rounds of 10-fold cross validation are shown in Table 5 .…”

Section: Resultsmentioning

confidence: 99%

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Discriminative Structure Learning of Bayesian Network Classifiers from Training Dataset and Testing Instance

Wang

Liu

Mammadov

et al. 2019

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Over recent decades, the rapid growth in data makes ever more urgent the quest for highly scalable Bayesian networks that have better classification performance and expressivity (that is, capacity to respectively describe dependence relationships between attributes in different situations). To reduce the search space of possible attribute orders, k-dependence Bayesian classifier (KDB) simply applies mutual information to sort attributes. This sorting strategy is very efficient but it neglects the conditional dependencies between attributes and is sub-optimal. In this paper, we propose a novel sorting strategy and extend KDB from a single restricted network to unrestricted ensemble networks, i.e., unrestricted Bayesian classifier (UKDB), in terms of Markov blanket analysis and target learning. Target learning is a framework that takes each unlabeled testing instance P as a target and builds a specific Bayesian model Bayesian network classifiers (BNC) P to complement BNC T learned from training data T . UKDB respectively introduced UKDB P and UKDB T to flexibly describe the change in dependence relationships for different testing instances and the robust dependence relationships implicated in training data. They both use UKDB as the base classifier by applying the same learning strategy while modeling different parts of the data space, thus they are complementary in nature. The extensive experimental results on the Wisconsin breast cancer database for case study and other 10 datasets by involving classifiers with different structure complexities, such as Naive Bayes (0-dependence), Tree augmented Naive Bayes (1-dependence) and KDB (arbitrary k-dependence), prove the effectiveness and robustness of the proposed approach.

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“…We compared the proposed algorithm when

Section: Resultsmentioning

confidence: 99%

“…Pernkopf and Bilmes [ 22 ] proposed a greedy heuristic strategy to determine the attribute order by comparing

where

ranks higher than

in the order, i.e.,

. Taheri et al [ 23 ] proposed to build a dynamic structure without specifying k a priori, and they proved that the resulting BNC is optimal.…”

Section: Bayesian Network and Markov Blanketmentioning

confidence: 99%

Discriminative Structure Learning of Bayesian Network Classifiers from Training Dataset and Testing Instance

Wang

Liu

Mammadov

et al. 2019

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“…Jing et al [31] presented the boosted BNC which greedily builds the structure with the arcs having the highest value of CMI. Taheri and Mammadov [32] provided an algorithm for adding arcs by using conditional probability of attributes given their parents. These BNCs are 1-dependence classifiers without a prior attribute order.…”

Section: Prior Workmentioning

confidence: 99%

Robust Structure Learning of Bayesian Network by Identifying Significant Dependencies

et al. 2019

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Bayesian networks have long been a popular medium for graphically representing the probabilistic dependencies which exist in a domain. State-of-the-art tree-augmented naive Bayes (TAN) builds maximum weighted spanning tree to represent 1-dependence relationships between attributes. In this paper, we propose to optimize the structure of TAN applying heuristic search to sort attribute and filtering technique to remove weak conditional dependencies. Extensive experimental results on 35 data sets from University of California at Irvine (UCI) machine learning repository reveal that the proposed algorithm achieves competitive generalization performance and even outperforms higher-dependence BNCs like kdependence Bayesian network while retaining excellent strucutre complexity. INDEX TERMS Bayesian network, heuristic search, conditional dependence filtering.

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“…Building a BN from data is called a learning process, and involves two steps: parametric learning and structured learning. [9] Structured learning has been more frequently studied than parametric learning. Common structured learning methods using BNs are the exhaustive method, hill-climbing algorithm, and K2 algorithm.…”

Section: Introductionmentioning

confidence: 99%

Application of a Tabu search-based Bayesian network in identifying factors related to hypertension

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Structure learning of Bayesian Networks using global optimization with applications in data classification

Cited by 12 publications

References 30 publications

Discriminative Structure Learning of Bayesian Network Classifiers from Training Dataset and Testing Instance

Discriminative Structure Learning of Bayesian Network Classifiers from Training Dataset and Testing Instance

Robust Structure Learning of Bayesian Network by Identifying Significant Dependencies

Application of a Tabu search-based Bayesian network in identifying factors related to hypertension

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