Data Science and Machine Learning have become fundamental assets for companies and research institutions alike. As one of its fields, supervised classification allows for class prediction of new samples, learning from given training data. However, some properties can cause datasets to be problematic to classify. In order to evaluate a dataset a priori, data complexity metrics have been used extensively. They provide information regarding different intrinsic characteristics of the data, which serve to evaluate classifier compatibility and a course of action that improves performance. However, most complexity metrics focus on just one characteristic of the data, which can be insufficient to properly evaluate the dataset towards the classifiers’ performance. In fact, class overlap, a very detrimental feature for the classification process (especially when imbalance among class labels is also present) is hard to assess. This research work focuses on revisiting complexity metrics based on data morphology. In accordance to their nature, the premise is that they provide both good estimates for class overlap, and great correlations with the classification performance. For that purpose, a novel family of metrics has been developed. Being based on ball coverage by classes, they are named after Overlap Number of Balls. Finally, some prospects for the adaptation of the former family of metrics to singular (more complex) problems are discussed.

数据科学和机器学习已成为公司和研究机构的基本资产。作为其领域之一，监督分类允许对新样本进行类别预测，从给定的训练数据中学习。但是，某些属性可能会导致数据集难以分类。为了先验地评估数据集，已广泛使用数据复杂性度量。它们提供有关数据不同内在特征的信息，用于评估分类器兼容性和提高性能的行动方案。然而，大多数复杂性指标仅关注数据的一个特征，这不足以根据分类器的性能正确评估数据集。事实上，阶级重叠，分类过程的一个非常不利的特征（尤其是当类标签之间也存在不平衡时）很难评估。这项研究工作的重点是重新审视基于数​​据形态的复杂性指标。根据它们的性质，前提是它们为类重叠提供了很好的估计，并且与分类性能有很大的相关性。为此，开发了一系列新颖的指标。基于类的球覆盖率，它们以重叠球数命名。最后，讨论了将前一族指标应用于单一（更复杂）问题的一些前景。前提是它们为类重叠提供了很好的估计，并且与分类性能有很大的相关性。为此，开发了一系列新颖的指标。基于类的球覆盖率，它们以重叠球数命名。最后，讨论了将前一族指标应用于单一（更复杂）问题的一些前景。前提是它们为类重叠提供了很好的估计，并且与分类性能有很大的相关性。为此，开发了一系列新颖的指标。基于类的球覆盖率，它们以重叠球数命名。最后，讨论了将前一族指标应用于单一（更复杂）问题的一些前景。