In this paper, multivalued data or multiple values variables are defined. They are typical when there is some intrinsic uncertainty in data production, as the result of imprecise measuring instruments, such as in image recognition, in human judgments and so on. So far, contributions in symbolic data analysis literature provide data preprocessing criteria allowing for the use of standard methods such as factorial analysis, clustering, discriminant analysis, tree-based methods. As an alternative, this paper introduces a methodology for supervised classification, the so-called Dynamic CLASSification TREE (D-CLASS TREE), dealing simultaneously with both standard and multivalued data as well. For that, an innovative partitioning criterion with a tree-growing algorithm will be defined. Main result is a dynamic tree structure characterized by the simultaneous presence of binary and ternary partitions. A real world case study will be considered to show the advantages of the proposed methodology and main issues of the interpretation of the final results. A comparative study with other approaches dealing with the same types of data will be also shown. The comparison highlights that, even if the results are quite similar in terms of error rates, the proposed D-CLASS tree returns a more interpretable tree-based structure.

中文翻译：

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基于动态递归树的划分用于皮肤病变皮肤镜图像中的恶性黑色素瘤识别

在本文中，定义了多值数据或多值变量。当数据生产中存在一些固有的不确定性时，它们是典型的，这是由于测量仪器不精确，例如图像识别、人类判断等。到目前为止，符号数据分析文献中的贡献提供了数据预处理标准，允许使用标准方法，如因子分析、聚类、判别分析、基于树的方法。作为替代，本文介绍了一种监督分类方法，即所谓的动态分类树（D-CLASS TREE），它同时处理标准和多值数据。为此，将定义具有树生长算法的创新分区标准。主要结果是动态树结构，其特征是同时存在二元和三元分区。将考虑一个真实世界的案例研究，以展示所提出的方法的优势和最终结果解释的主要问题。还将展示与处理相同类型数据的其他方法的比较研究。比较突出表明，即使结果在错误率方面非常相似，所提出的 D-CLASS 树也返回了一个更具可解释性的基于树的结构。