Learning Bayesian network classifiers by maximizing conditional likelihood

Grossman, Daniel; Domingos, Pedro

doi:10.1145/1015330.1015339

Cited by 212 publications

(171 citation statements)

References 22 publications

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“…Instead of optimizing the loglikelihood, discriminative approaches try to maximize the conditional likelihood and may produce better class probability estimates [14], [24], [3]. A discriminant version of SNODE can be developed by maximizing the conditional likelihood; however, the corresponding optimization problem may be more difficult than the current one, since the conditional likelihood does not decompose into separate terms for each function to be learned.…”

Section: Discussionmentioning

confidence: 99%

Semi-naive Exploitation of One-Dependence Estimators

Zhou

2009

2009 Ninth IEEE International Conference on Data Mining

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Abstract-It is well known that the key of Bayesian classifier learning is to balance the two important issues, that is, the exploration of attribute dependencies in high orders for ensuring a sufficient flexibility in approximating the ground-truth dependencies, and the exploration of low orders for ensuring a stable probability estimate from limited training samples. By allowing one-order attribute dependencies, one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth attribute dependencies whilst keeping the effectiveness of probability estimation, and therefore leading to excellent performance. In previous studies, however, ODEs were exploited in simple ways, such as by averaging, for classification. In this paper, we propose a semi-naive exploitation of ODEs that fits a function of ODEs to pursue higher-order attribute dependencies. Extensive experiments show that the proposed SNODE approach can achieve better performance than many state-of-the-art Bayesian classifiers.

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Section: Discussionmentioning

confidence: 99%

Semi-naive Exploitation of One-Dependence Estimators

Zhou

2009

2009 Ninth IEEE International Conference on Data Mining

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“…A variety of multinomial classifiers have been proposed for handling an arbitrary number of independent attributes. Let us mention especially [45], [46], [34], semi-naive Bayesian classifiers [44], [19], tree-augmented naive Bayesian classifiers [32], k-dependence Bayesian classifiers [53], and Bayesian Network-augmented naive Bayesian classifiers [13].…”

Section: Appendix: Naive Bayesian Classifiersmentioning

confidence: 99%

Naive possibilistic classifiers for imprecise or uncertain numerical data

Bounhas

Hamed

Prade

et al. 2014

Fuzzy Sets and Systems

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In real-world problems, input data may be pervaded with uncertainty. In this paper, we investigate the behavior of naive possibilistic classifiers, as a counterpart to naive Bayesian ones, for dealing with classification tasks in presence of uncertainty. For this purpose, we extend possibilistic classifiers, which have been recently adapted to numerical data, in order to cope with uncertainty in data representation. Here the possibility distributions that are used are supposed to encode the family of Gaussian probabilistic distributions that are compatible with the considered data set. We consider two types of uncertainty: i) the uncertainty associated with the class in the training set, which is modeled by a possibility distribution over class labels, and ii) the imprecision pervading attribute values in the testing set represented under the form of intervals for continuous data. Moreover, the approach takes into account the uncertainty about the estimation of the Gaussian distribution parameters due to the limited amount of data available. We first adapt the possibilistic classification model, previously proposed for the certain case, in order to accommodate the uncertainty about class labels. Then, we propose an algorithm based on the extension principle to deal with imprecise attribute values. The experiments reported show the interest of possibilistic classifiers for handling uncertainty in data. In particular, the probability-to-possibility transform-based classifier shows a robust behavior when dealing with imperfect data.

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“…There are large number of classifiers that are used to classify the data such as bayes, function, rule based and Tree etc. The goal of classification is to correctly predict the value of a designated discrete class variable, given a vector of predictors or attributes [5].…”

Section: Classificationmentioning

confidence: 99%