Learning Bayesian network classifiers from label proportions

• A Structural-EM method to learn Bayesian network classifiers for the LLP problem.
• Variants of the method designed to deal with very-complex LLP scenarios.
• Only (joint) label assignments that fulfill the LP of the groups are considered.
• A framework for testing LLP methods that covers the spectrum of LLP complexities.
• Good performance behavior in different experimental settings.

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.