Abstract Feature selection is a key step in many machine learning tasks. A majority of the existing methods of feature selection address the problem by devising some scoring function while treating the features independently, thereby overlooking their interdependencies. We leverage the scale invariance property of copula to construct a greedy, supervised feature selection algorithm that maximizes the feature relevance while minimizing the redundant information content. Multivariate copula is used in the proposed copula Based Feature Selection (CBFS) to discover the dependence structure between features. The incorporation of copula-based multivariate dependency in the formulation of mutual information helps avoid averaging over multiple instances of bivariate dependencies, thus eliminating the average estimation error introduced when bivariate dependency is used between a pair of feature variables. Under a controlled setting, our algorithm outperformed the existing best practice methods in warding off the noise in data. On several real and synthetic datasets, the proposed algorithm performed competitively in maximizing classification accuracy. CBFS also outperforms the other methods in terms of its noise tolerance property.

中文翻译：

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使用基于 Copula 的互信息的稳定特征选择

摘要 特征选择是许多机器学习任务中的关键步骤。大多数现有的特征选择方法通过在独立处理特征的同时设计一些评分函数来解决这个问题，从而忽略了它们的相互依赖性。我们利用 copula 的尺度不变性来构建一个贪婪的、有监督的特征选择算法，该算法最大化特征相关性，同时最小化冗余信息内容。在提出的基于 copula 的特征选择 (CBFS) 中使用多变量 copula 来发现特征之间的依赖结构。在互信息的公式中结合基于 copula 的多变量依赖有助于避免对双变量依赖的多个实例进行平均，从而消除了在一对特征变量之间使用双变量依赖时引入的平均估计误差。在受控设置下，我们的算法在抵御数据噪声方面优于现有的最佳实践方法。在几个真实的和合成的数据集上，所提出的算法在最大化分类准确度方面具有竞争力。CBFS 在其噪声容限特性方面也优于其他方法。