A simple squared-error reformulation for ordinal classification

Christopher Beckham; Christopher Pal

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arXiv · 1612.00775

A simple squared-error reformulation for ordinal classification

Christopher Beckham·Christopher Pal

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Abstract

In this paper, we explore ordinal classification (in the context of deep neural networks) through a simple modification of the squared error loss which not only allows it to not only be sensitive to class ordering, but also allows the possibility of having a discrete probability distribution over the classes. Our formulation is based on the use of a softmax hidden layer, which has received relatively little attention in the literature. We empirically evaluate its performance on the Kaggle diabetic retinopathy dataset, an ordinal and high-resolution dataset and show that it outperforms all of the baselines employed.

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Christopher Beckham, Christopher Pal. 2016-12-02. A simple squared-error reformulation for ordinal classification. https://arxiv.org/abs/1612.00775

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