你的分类问题有多复杂？分类复杂性度量研究综述
How Complex is your classification problem? A survey on measuring classification complexity
摘要：
从分类问题的训练数据集中提取的特征已被证明是一些荟萃分析中的有效预测因子。其中，分类复杂性的度量可以用来估计将数据点分离到其期望类别中的难度。数据的空间分布的描述符和决策边界的形状和大小的估计是用于该表征的已知测量。这些信息可以支持制定新的数据驱动的预处理和模式识别技术，而这些技术又可以侧重于问题的这些特点所突出的挑战。本文调查和分析的措施，可以从训练数据集中提取，以表征各自的分类问题的复杂性。他们在最近的文献中的使用也进行了审查和讨论，允许在该领域的未来工作的前景机会。最后，描述了一个名为扩展复杂性库（ECoL）的R包，它实现了一组复杂性度量，并公开提供。
英文摘要：
Characteristics extracted from the training datasets of classification problems have proven to be effective predictors in a number of meta-analyses. Among them, measures of classification complexity can be used to estimate the difficulty in separating the data points into their expected classes. Descriptors of the spatial distribution of the data and estimates of the shape and size of the decision boundary are among the known measures for this characterization. This information can support the formulation of new data-driven pre-processing and pattern recognition techniques, which can in turn be focused on challenges highlighted by such characteristics of the problems. This paper surveys and analyzes measures which can be extracted from the training datasets in order to characterize the complexity of the respective classification problems. Their use in recent literature is also reviewed and discussed, allowing to prospect opportunities for future work in the area. Finally, descriptions are given on an R package named Extended Complexity Library (ECoL) that implements a set of complexity measures and is made publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
