Abstract
Learning from label proportions is the term used for the learning paradigm where the training data is provided in groups (or “bags”), and only the label proportion for each bag is known. The objective is to learn a model to predict the class labels of individual instances. This paradigm presents very different applications, specially concerning anonymous data. Two different iterative strategies are proposed to deal with this type of problems, both based on optimising the class membership of the instances using the estimated pattern distribution per bag and the label proportions. Discriminant analysis is reformulated to deal with non-crisp class memberships. A thorough set of experiments is conducted to test: (1) the performance gap between these approaches and the fully supervised setting, (2) the potential advantages of optimising class memberships by our proposals, and (3) the influence of factors such as the bag size and the number of classes of the problem in the performance.
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Acknowledgment
This work was financed by the TIN2014-54583-C2-1-R project of the Spanish MINECO, by FEDER Funds and by the P11-TIC-7508 project of the Junta de Andalucía, Spain.
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Pérez-Ortiz, M., Gutiérrez, P.A., Carbonero-Ruz, M., Hervás-Martínez, C. (2016). Learning from Label Proportions via an Iterative Weighting Scheme and Discriminant Analysis. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_8
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DOI: https://doi.org/10.1007/978-3-319-44636-3_8
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