Quantification using permutation-invariant networks based on histograms

Pérez-Mon O., Moreo Fernandez A., Del Coz J. J., González P.

Quantification  Prevalence estimation  Deep learning  Deep neural networks 

Quantification, also known as class prevalence estimation, is the supervised learning task in which a model is trained to predict the prevalence of each class in a given bag of examples. This paper investigates the application of deep neural networks for tasks of quantification in scenarios where it is possible to apply a symmetric supervised approach that eliminates the need for classification as an intermediate step, thus directly addressing the quantification problem. Additionally, it discusses existing permutation-invariant layers designed for set processing and assesses their suitability for quantification. Based on our analysis, we propose HistNetQ, a novel neural architecture that relies on a permutation-invariant representation based on histograms that is especially suited for quantification problems. Our experiments carried out in two standard competitions, which have become a reference in the quantification field, show that HistNetQ outperforms other deep neural network architectures designed for set processing, as well as the current state-of-the-art quantification methods. Furthermore, HistNetQ offers two significant advantages over traditional quantification methods: i) it does not require the labels of the training examples but only the prevalence values of a collection of training bags, making it applicable to new scenarios; and ii) it is able to optimize any custom quantification-oriented loss function.

Source: NEURAL COMPUTING & APPLICATIONS

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