Regularization oversampling for classification tasks: To exploit what you do not know
Van der Schraelen, Lennert Stouthuysen, Kristof Vanden Broucke, Seppe Verdonck, Tim
Van der Schraelen, Lennert
Stouthuysen, Kristof
Vanden Broucke, Seppe
Verdonck, Tim
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Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2023
Journal
Information Sciences
Book
Publication Volume
635
Publication Issue
July
Publication Begin page
169
Publication End page
194
Publication Number of pages
Collections
Abstract
In numerous binary classification tasks, the two groups of instances are not equally represented, which often implies that the training data lack sufficient information to model the minority class correctly. Furthermore, many traditional classification models make arbitrarily overconfident predictions outside the range of the training data. These issues severely impact the deployment and usefulness of these models in real life. In this paper, we propose the boundary regularizing out-of-distribution (BROOD) sampler, which adds artificial data points on the edge of the training data. By exploiting these artificial samples, we are able to regularize the decision surface of discriminative machine learning models and make more prudent predictions. Next, it is crucial to correctly classify many positive instances in a limited pool of instances that can be investigated with the available resources. By smartly assigning predetermined nonuniform class probabilities outside the training data, we can emphasize certain data regions and improve classifier performance on various material classification metrics. The good performance of the proposed methodology is illustrated in a case study that consists of both benchmark balanced and imbalanced classification data sets.
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Keywords
Binary Classification, Regularization, Sampling, Data Imbalance