Full Bayesian network classifiers

Su, Jiang; Zhang, Harry

doi:10.1145/1143844.1143957

Cited by 58 publications

(37 citation statements)

References 10 publications

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“…We now demonstrate that it is easy to incorporate this consideration into our optimization program in (23).…”

Section: E Assigning Costs To the Selection Of Edgesmentioning

confidence: 93%

“…However, estimating the model parameters via maximum-likelihood is complicated because the learned structures are loopy. Su and Zhang [23] suggested representing variable independencies by conditional probability tables (CPT) instead of the structures of graphical models. Boosting has been used in Rosset and Segal [24] for density estimation and learning Bayesian networks, but the objective was on modeling and not on classification.…”

Section: B Related Workmentioning

confidence: 99%

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Learning Graphical Models for Hypothesis Testing

Sanghavi

Tan

Willsky

2007

2007 IEEE/SP 14th Workshop on Statistical Signal Processing

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Abstract-Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques to exploit this modeling ability for binary classification by discriminatively learning such models from labeled training data, i.e., using both positive and negative samples to optimize for the structures of the two models. We motivate why it is difficult to adapt existing generative methods, and propose an alternative method consisting of two parts. First, we develop a novel method to learn tree-structured graphical models which optimizes an approximation of the log-likelihood ratio. We also formulate a joint objective to learn a nested sequence of optimal forests-structured models. Second, we construct a classifier by using ideas from boosting to learn a set of discriminative trees. The final classifier can interpreted as a likelihood ratio test between two models with a larger set of pairwise features. We use cross-validation to determine the optimal number of edges in the final model. The algorithm presented in this paper also provides a method to identify a subset of the edges that are most salient for discrimination. Experiments show that the proposed procedure outperforms generative methods such as Tree Augmented Naïve Bayes and Chow-Liu as well as their boosted counterparts.

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“…We now demonstrate that it is easy to incorporate this consideration into our optimization program in (23).…”

Section: E Assigning Costs To the Selection Of Edgesmentioning

confidence: 93%

Section: B Related Workmentioning

confidence: 99%

Learning Graphical Models for Hypothesis Testing

Sanghavi

Tan

Willsky

2007

2007 IEEE/SP 14th Workshop on Statistical Signal Processing

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“…Finally, in FBN, all variables are dependent. In this study, we used the algorithm proposed in [14] to build FBNs. Attributes in this algorithm are assumed to be dependent on each other, and attribute independence is captured in CPTs (conditional probability tables) learned from decision trees.…”

Section: Methodsmentioning

confidence: 99%

Statistical Approaches to Predicting and Diagnosing Performance Problems in Component-Based Distributed Systems: An Experimental Evaluation

Corrêa

Cerqueira

2010

2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems

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Abstract-One of the major problems in managing largescale distributed systems is the prediction of the application performance. The complexity of the systems and the availability of monitored data have motivated the applicability of machine learning and other statistical techniques to induce performance models and forecast performance degradation problems. However, there is a stringent need for additional experimental and comparative studies, since there is no optimal method for all cases. In addition to a deeper comparison of different statistical techniques, studies lack on two important dimensions: resilience to transient failures of the statistical techniques, and diagnostic abilities. In this work, we address these issues, presenting three main contributions: first, we establish the capability of different statistical learning techniques for forecasting the resource needs of component-based distributed systems; second, we investigate an analysis engine that is more robust to false alarms, introducing a novel algorithm that augments the predictive power of statistical learning methods by combining them with a statistical test to identify trends in resources usage; third, we investigate the applicability of statistical tests for identifying the nature and cause of performance problems in component-based distributed systems.

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“…Thus, heuristic and approximate learning algorithms are the realistic solution. A variety of learning algorithms have been proposed 26 . Moreover, it has been observed that learning an unrestricted Bayesian network classifier seems to not necessarily lead to a classifier with good performance.…”

Section: Introductionmentioning

confidence: 99%

Locally Weighted Learning: How and When Does it Work in Bayesian Networks?

Wu¹,

Wu²,

Pan³

et al. 2015

IJCIS

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Bayesian network (BN), a simple graphical notation for conditional independence assertions, is promised to represent the probabilistic relationships between diseases and symptoms. Learning the structure of a Bayesian network classifier (BNC) encodes conditional independence assumption between attributes, which may deteriorate the classification performance. One major approach to mitigate the BNC's primary weakness (the attributes independence assumption) is the locally weighted approach. And this type of approach has been proved to achieve good performance for naive Bayes, a BNC with simple structure. However, we do not know whether or how effective it works for improving the performance of the complex BNC. In this paper, we first do a survey on the complex structure models for BNCs and their improvements, then carry out a systematically experimental analysis to investigate the effectiveness of locally weighted method for complex BNCs, e.g., tree-augmented naive Bayes (TAN), averaged one-dependence estimators AODE and hidden naive Bayes (HNB), measured by classification accuracy (ACC) and the area under the ROC curve ranking (AUC). Experiments and comparisons on 36 benchmark data sets collected from University of California, Irvine (UCI) in Weka system demonstrate that locally weighting technologies just slightly outperforms unweighted complex BNCs on ACC and AUC. In other words, although locally weighting could significantly improve the performance of NB (a BNC with simple structure), it could not work well on BNCs with complex structures. This is because the performance improvements of BNCs are attributed to their structures not the locally weighting.

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Full Bayesian network classifiers

Cited by 58 publications

References 10 publications

Learning Graphical Models for Hypothesis Testing

Learning Graphical Models for Hypothesis Testing

Statistical Approaches to Predicting and Diagnosing Performance Problems in Component-Based Distributed Systems: An Experimental Evaluation

Locally Weighted Learning: How and When Does it Work in Bayesian Networks?

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