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

TL;DR

This paper introduces the D-CLASS TREE, a novel dynamic recursive tree method for classifying multivalued and standard data, demonstrating superior accuracy in skin lesion melanoma identification.

Contribution

The paper presents a new supervised classification methodology that handles both standard and multivalued data using a dynamic tree structure with mixed binary and ternary partitions.

Findings

D-CLASS TREE outperforms competitors in accuracy.

The method effectively manages multivalued data in skin lesion analysis.

Real-world case study validates the approach's advantages.

Abstract

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.