A Deep Learning Approach to Unsupervised Ensemble Learning
This work addresses the problem of improving label estimation in crowdsourcing and ensemble learning for researchers and practitioners, offering a novel deep learning method that is particularly effective when conditional independence assumptions are violated.
The paper tackles the problem of unsupervised ensemble learning by showing that a popular model is equivalent to a Restricted Boltzmann Machine (RBM) and proposing an RBM-based Deep Neural Net (DNN) for cases where classifiers violate conditional independence, with experimental results demonstrating that the DNN approach outperforms state-of-the-art methods, especially under such violations.
We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning. First, we prove that the popular model of Dawid and Skene, which assumes that all classifiers are conditionally independent, is {\em equivalent} to a Restricted Boltzmann Machine (RBM) with a single hidden node. Hence, under this model, the posterior probabilities of the true labels can be instead estimated via a trained RBM. Next, to address the more general case, where classifiers may strongly violate the conditional independence assumption, we propose to apply RBM-based Deep Neural Net (DNN). Experimental results on various simulated and real-world datasets demonstrate that our proposed DNN approach outperforms other state-of-the-art methods, in particular when the data violates the conditional independence assumption.