Vine copula classifiers for the mind reading problem

In this paper we introduce vine copulas to model probabilistic dependencies in supervised classification problems. Vine copulas allow the representation of the dependence structure of multidimensional distributions as a factorization of bivariate pair-copulas. The flexibility of this model lies in the fact that we can mix different types of pair-copulas in a factorization, which allows covering a wide range of types of dependencies, i.e., from independence to much more complex forms of bivariate correlations. This property motivates us to use vine copulas as classifiers, particularly for problems for which the type and strength of bivariate interactions between the variables show a great variability. This is the case of brain signal classification problems where information is represented as multiple time series, each one recorded from different brain region. Our experimental results on a real-word Mind Reading Problem reveal that vine copula-based classifiers perform competitively compared to the four best classification methods presented at the Mind Reading Challenge Competition 2011.

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