Abstract The use of crowdsourcing for annotating data has become a popular and cheap alternative to expert labelling. As a consequence, an aggregation task is required to combine the different labels provided and agree on a single one per example. Most aggregation techniques, including the simple and robust majority voting—to select the label with the largest number of votes—disregard the descriptive information provided by the explanatory variable. In this paper, we propose domain-aware voting, an extension of majority voting which incorporates the descriptive variable and the rest of the instances of the dataset for aggregating the label of every instance. The experimental results with simulated and real-world crowdsourced data suggest that domain-aware voting is a competitive alternative to majority voting, especially when a part of the dataset is unlabelled. We elaborate on practical criteria for the use of domain-aware voting.

Weakly supervised learning is aimed to learn predictive models from partially supervised data, an easy-to-collect alternative to the costly standard full supervision. During the last decade, the research community has striven to show that learning reliable models in specific weakly supervised problems is possible. We present an empirical study that analyzes the value of weak information of supervision throughout its entire spectrum, from none to full supervision. Its contribution is assessed under the realistic assumption that a small subset of fully supervised data is available. Particularized in the problem of learning with candidate sets, we adapt Cozman and Cohen [1] key study to learning from weakly supervised data. Standard learning techniques are used to infer generative models from this type of supervision with both synthetic and real data. Empirical results suggest that weakly labeled data is helpful in realistic scenarios, where fully labeled data is scarce, and its contribution is directly related to both the amount of information of supervision and how meaningful this information is.

Majority voting is a popular and robust strategy to aggregate different opinions in learning from crowds, where each worker labels examples according to their own criteria. Although it has been extensively studied in the binary case, its behavior with multiple classes is not completely clear, specifically when annotations are biased. This paper attempts to fill that gap. The behavior of the majority voting strategy is studied in-depth in multi-class domains, emphasizing the effect of annotation bias. By means of a complete experimental setting, we show the limitations of the standard majority voting strategy. The use of three simple techniques that infer global information from the annotations and annotators allows us to put the performance of the majority voting strategy in context.

Weakly supervised classification tries to learn from data sets which are not certainly labeled. Many problems, with different natures of partial labeling, fit this description. In this paper, the novel problem of learning from positive-unlabeled proportions is presented. The provided examples are unlabeled, and the only class information available consists of the proportions of positive and unlabeled examples in different subsets of the training data set. We present a methodology that adapts to the different levels of class uncertainty to learn Bayesian network classifiers using an expectation-maximization strategy. It has been tested in a variety of artificial scenarios with different class uncertainty, as well as compared with two naive strategies that do not consider all the available class information. Finally, it has also been successfully tested in real data, collected from the embryo selection problem in assisted reproduction.

Learning from crowds is a classification problem where the provided training instances are labeled by multiple (usually conflicting) annotators. In different scenarios of this problem, straightforward strategies show an astonishing performance. In this paper, we characterize the crowd scenarios where these basic strategies show a good behavior. As a consequence, this study allows to identify those scenarios where non-basic methods for combining the multiple labels are expected to obtain better results. In this context, we extend the learning from crowds paradigm to the multidimensional (MD) classification domain. Measuring the quality of the annotators, the presented EM-based method overcomes the lack of a fully reliable labeling for learning MD Bayesian network classifiers: As the expertise is identified and the contribution of the relevant annotators promoted, the model parameters are optimized. The good performance of our proposal is demonstrated throughout different sets of experiments.