Vine Copula-Based Classifiers with Applications

Özge Şahin () and Harry Joe
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Özge Şahin: Delft University of Technology
Harry Joe: University of British

Journal of Classification, 2025, vol. 42, issue 2, No 4, 335-363

Abstract: Abstract The vine pair-copula construction can be used to fit flexible non-Gaussian multivariate distributions to a mix of continuous and discrete variables. With multiple classes, fitting univariate distributions and a vine to each class lead to posterior probabilities over classes that can be used for discriminant analysis. This is more flexible than methods with the Gaussian and/or independence assumptions, such as quadratic discriminant analysis and naive Bayes. Some variable selection methods are studied to accompany the vine copula-based classifier because unimportant variables can make discrimination worse. Simple numerical performance metrics cannot give a full picture of how well a classifier is doing. We introduce categorical prediction intervals and other summary measures to assess the difficulty of discriminating classes. Through extensive experiments on real data, we demonstrate the superior performance of our approaches compared to traditional discriminant analysis methods and random forests when features have different dependent structures for different classes.

Keywords: Classification; Copula; Feature selection; Prediction interval; Statistical learning; Vine (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00357-024-09494-y

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