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A distribution-free smoothed combination method to improve discrimination accuracy in multi-category classification

Maiti, Raju; Li, Jialiang; Das, Priyam; Liu, Xueqing; Feng, Lei; Hausenloy, Derek J; Chakraborty, Bibhas; (2022) A distribution-free smoothed combination method to improve discrimination accuracy in multi-category classification. Statistical Methods in Medical Research 10.1177/09622802221137742. (In press). Green open access

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Abstract

Results from multiple diagnostic tests are combined in many ways to improve the overall diagnostic accuracy. For binary classification, maximization of the empirical estimate of the area under the receiver operating characteristic curve has widely been used to produce an optimal linear combination of multiple biomarkers. However, in the presence of a large number of biomarkers, this method proves to be computationally expensive and difficult to implement since it involves maximization of a discontinuous, non-smooth function for which gradient-based methods cannot be used directly. The complexity of this problem further increases when the classification problem becomes multi-category. In this article, we develop a linear combination method that maximizes a smooth approximation of the empirical Hyper-volume Under Manifolds for the multi-category outcome. We approximate HUM by replacing the indicator function with the sigmoid function and normal cumulative distribution function. With such smooth approximations, efficient gradient-based algorithms are employed to obtain better solutions with less computing time. We show that under some regularity conditions, the proposed method yields consistent estimates of the coefficient parameters. We derive the asymptotic normality of the coefficient estimates. A simulation study is performed to study the effectiveness of our proposed method as compared to other existing methods. The method is illustrated using two real medical data sets.

Type: Article
Title: A distribution-free smoothed combination method to improve discrimination accuracy in multi-category classification
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/09622802221137742
Publisher version: https://doi.org/10.1177/09622802221137742
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Pre-clinical and Fundamental Science
URI: https://discovery.ucl.ac.uk/id/eprint/10161235
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