Abstract:
Because ensemble learning improves classification model prediction, it has many applications. This article proposes semisupervised multiple choice learning (SemiMCL) to jointly train a network ensemble on partially labeled data. Our model improves labeled data assignment among constituent networks and uses unlabeled data to capture domain-specific information for semisupervised classification. The constituent networks train multiple tasks, unlike conventional multiple choice learning models. An auxiliary reconstruction task teaches domain-specific representation. We minimize conditional entropy with respect to the posterior probability distribution using a negative l1-norm regularization for implicit labeling on reliable unlabeled samples. The SemiMCL model is tested on multiple real-world datasets.
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