Dynamic recursive tree-based partitioning for malignant melanoma identification in skin lesion dermoscopic images

  • Massimo Aria
  • Antonio D’Ambrosio
  • Carmela Iorio
  • Roberta Siciliano
  • Valentina Cozza
Regular Article


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.


Classification trees Multivalued data Melanoma recognition Predictive learning 



Authors would like to thank Prof. A. Baroni of the Campania University “Luigi Vanvitelli” (Italy) for kindly providing us the Skin lesions data set. Authors would like to thank two anonymous reviewers whose comments highly contribute to improve the quality of the manuscript.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Economics and StatisticsUniversity of Naples Federico IINaplesItaly
  2. 2.Department of Industrial EngineeringUniversity of Naples Federico IINaplesItaly
  3. 3.Department of LawParthenope University of NaplesNaplesItaly

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