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    Regularization oversampling for classification tasks: To exploit what you do not know

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    Publication type
    Journal article with impact factor
    Author
    Van der Schraelen, Lennert
    Stouthuysen, Kristof
    Vanden Broucke, Seppe
    Verdonck, Tim
    Publication Year
    2023
    Journal
    Information Sciences
    Publication Volume
    635
    Publication Issue
    July
    Publication Begin page
    169
    Publication End page
    194
    
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    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.
    Keyword
    Binary Classification, Regularization, Sampling, Data Imbalance
    Knowledge Domain/Industry
    Accounting & Finance
    DOI
    10.1016/j.ins.2023.03.146
    URI
    http://hdl.handle.net/20.500.12127/7237
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.ins.2023.03.146
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