A Deep Learning Approach to Unsupervised Ensemble Learning

Abstract

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.

References in corpus (3)

• Improving neural networks by preventing co-adaptation of feature detectors
• An exact mapping between the Variational Renormalization Group and Deep Learning
• Why are deep nets reversible: A simple theory, with implications for training

Cited by in corpus (8)

• Ensemble deep learning: A review
• Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects
• Deep ensemble learning for Alzheimers disease classification
• Wisdom of the crowd from unsupervised dimension reduction
• Probabilistic Ensemble of Collaborative Filters
• A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines
• Agreement-based Learning
• Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface