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Vine copula classifiers for the mind reading problem

机译：葡萄阅读器分类器

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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 showa 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 MindReading Problem reveal that vine copula-based classifiers perform competitively compared to the four best classification methods presented at the Mind Reading Challenge Competition 2011.
机译：在本文中，我们介绍了藤蔓copulas在监督分类问题中对概率依赖性进行建模。藤copulas表示多维分布的依存结构作为双变量对copulas的分解。该模型的灵活性在于我们可以在因式分解中混合使用不同类型的对-对数，从而可以涵盖多种类型的依赖关系，即从独立性到更为复杂的双变量相关性形式。此属性促使我们使用藤蔓copulas作为分类器，特别是对于变量之间的双变量交互的类型和强度表现出很大变异性的问题。这是脑信号分类问题的情况，其中信息表示为多个时间序列，每个时间序列都记录在不同的大脑区域。我们在真实单词MindReading Problem上的实验结果表明，与在2011年Mind Reading Challenge竞赛中提出的四种最佳分类方法相比，基于葡萄树copula的分类器具有竞争优势。

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