  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. \noindent 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.
D-CLASS TREE outperforms its competitors in terms of accuracy, which is a
fundamental aspect for predictive learning.
