Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Machine Learning - Special issue on learning with probabilistic representations
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Learning Bayesian classifiers from positive and unlabeled examples
Pattern Recognition Letters
Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm
Decision Support Systems
Supervised Learning by Training on Aggregate Outputs
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Selection of human embryos for transfer by Bayesian classifiers
Computers in Biology and Medicine
Kernel K-means Based Framework for Aggregate Outputs Classification
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Estimating Labels from Label Proportions
The Journal of Machine Learning Research
Learning Bayesian networks from incomplete data
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Learning from label proportions by optimizing cluster model selection
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Learning Bayesian networks from incomplete data with stochastic search algorithms
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning Bayesian networks from incomplete databases
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning naive Bayes models for multiple-instance learning with label proportions
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
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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.