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Discriminative structure learning of Bayesian network classifiers from training dataset and testing instance

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posted on 2019-05-01, 00:00 authored by L Wang, Y Liu, Musa MammadovMusa Mammadov, M Sun, S Qi
© 2019 by the authors. 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 BNCT learned from training data T . UKDB respectively introduced UKDBP and UKDBT 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.

History

Journal

Entropy

Volume

21

Issue

5

Publisher

MDPI

Location

Basel, Switzerland

ISSN

1099-4300

eISSN

1099-4300

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2019, The Authors

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