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In multi-class text classification, the performance (effectiveness) of a classifier is usually measured by micro-averaged and macro-averaged F1 scores. However, the scores themselves do not tell us how reliable they are in terms of forecasting the classifier's future performance on unseen data. In this paper, we propose a novel approach to explicitly modelling the uncertainty of average F1 scores...
In many practical machine learning systems, the prediction/classification tasks involve the usage of heterogeneous data in semi-supervised settings, where the objective is to maximize the utility of multiple views (usually dual views) information from the data. In this work, we propose a general framework, Dual Uncertainty Minimization Regularization (DUMR), that maximizes the usage of heterogeneous...
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