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Learning Bayesian network classifiers from label proportions
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DOI：10.1016/j.patcog.2013.05.002.png)
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Abstract
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This paper deals with a classification problem known as learning from label proportions. The provided dataset is composed of unlabeled instances and is divided into disjoint groups. General class information is given within the groups: the proportion of instances of the group that belong to each class. We have developed a method based on the Structural EM strategy that learns Bayesian network classifiers to deal with the exposed problem. Four versions of our proposal are evaluated on synthetic data, and compared with state-of-the-art approaches on real datasets from public repositories. The results obtained show a competitive behavior for the proposed algorithm. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Supervised classification
Learning from label proportions
Structural EM algorithm
Bayesian network classifiers
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