Extended Tree Augmented Naive Classifier

Campos, Cassio P. de; Cuccu, Marco; Corani, Giorgio; Zaffalon, Marco

doi:10.1007/978-3-319-11433-0_12

Cited by 11 publications

(20 citation statements)

References 19 publications

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“…In this paper, we adapt an existing approach to learning extended tree augmented naive Bayesian classifiers (ETANs) in general [6], to learning the combined bridge and feature subgraph of a multi-classifier. First a feature subgraph is learned on a copy X of the set of feature variables X.…”

Section: Preliminariesmentioning

confidence: 99%

“…Using a standard Bayesian network learner [1] or a modified version of the ETANalgorithm [6], we now learn feature subgraph on X , where each feature variable X i is allowed at most one parent. For each variable X i , the chosen parent X j (or the empty parent set) is then expanded to the parent set of the original score.…”

Section: Preliminariesmentioning

confidence: 99%

“…In Sect. 6 we report results achieved with our algorithm on various multi-label data sets. The paper is rounded off with our conclusions and directions for future work in Sect.…”

Section: Introductionmentioning

confidence: 99%

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Multi-classifiers of Small Treewidth

Pastink

Gaag

2015

Lecture Notes in Computer Science

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Abstract. Multi-dimensional Bayesian network classifiers are becoming quite popular for multi-label classification. These models have the advantage of a high expressive power, but may induce a prohibitively high runtime of classification. We argue that the high runtime burden originates from their large treewidth. Thus motivated, we present an algorithm for learning multi-classifiers of small treewidth. Experimental results show that these models have a small runtime of classification, without loosing accuracy compared to unconstrained multi-classifiers.

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Section: Preliminariesmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

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Multi-classifiers of Small Treewidth

Pastink

Gaag

2015

Lecture Notes in Computer Science

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“…Based on the symmetry property (equation (6)), we can have an efficient algorithm to find the optimal TAN structure by converting the original problem (equation (5)) into a minimum spanning tree construction problem. More details could be found in [9].…”

Section: The Tree Augmented Naive Bayes Classifiermentioning

confidence: 99%

“…a higher field similarity value indicating a higher degree of 'match') can be incorporated to help reduce overfitting in classification [8]. Recently, a state-of-the-art Bayesian network classifier called ETAN [9,10] has been proposed and shown outperform the NBC and TAN in many cases. ETAN relaxes the assumption about independence of features, and does not require features to be connected to the class.…”

Section: Introductionmentioning

confidence: 99%

Extending Naive Bayes Classifier with Hierarchy Feature Level Information for Record Linkage

Zhou

Howroyd

Danicic

et al. 2015

Advanced Methodologies for Bayesian Networks

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Abstract. Probabilistic record linkage has been well investigated in recent years. The Fellegi-Sunter probabilistic record linkage and its enhanced version are commonly used methods, which calculate match and non-match weights for each pair of corresponding fields of record-pairs. Bayesian network classifiers -naive Bayes classifier and TAN have also been successfully used here. Very recently, an extended version of TAN (called ETAN) has been developed and proved superior in classification accuracy to conventional TAN. However, no previous work has applied ETAN in record linkage and investigated the benefits of using a naturally existing hierarchy feature level information. In this work, we extend the naive Bayes classifier with such information. Finally we apply all the methods to four datasets and estimate the F1 scores.

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Trading off Speed and Accuracy in Multilabel Classification

Corani

Antonucci

Mauá

et al. 2014

Probabilistic Graphical Models

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In previous work, we devised an approach for multilabel classification based on an ensemble of Bayesian networks. It was characterized by an efficient structural learning and by high accuracy. Its shortcoming was the high computational complexity of the MAP inference, necessary to identify the most probable joint configuration of all classes. In this work, we switch from the ensemble approach to the single model approach. This allows important computational savings. The reduction of inference times is exponential in the difference between the treewidth of the single model and the number of classes. We adopt moreover a more sophisticated approach for the structural learning of the class subgraph. The proposed single models outperforms alternative approaches for multilabel classification such as binary relevance and ensemble of classifier chains.

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Extended Tree Augmented Naive Classifier

Cited by 11 publications

References 19 publications

Multi-classifiers of Small Treewidth

Multi-classifiers of Small Treewidth

Extending Naive Bayes Classifier with Hierarchy Feature Level Information for Record Linkage

Trading off Speed and Accuracy in Multilabel Classification

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