Splitting criteria for ordinal decision trees: an experimental study

作     者：Ayllón-Gavilán, Rafael Martínez-Estudillo, Francisco José Guijo-Rubio, David Hervás-Martínez, César Gutiérrez, Pedro Antonio 

作者机构：Department of Computer Science and Numerical Analysis Universidad de Córdoba Campus de Rabanales Ctra. N-IVa Km. 396 Córdoba14071 Spain Department of Quantitative Methods Universidad Loyola Andalucía C. Escritor Castilla Aguayo 4 Córdoba14004 Spain 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Adversarial machine learning 

摘      要：Ordinal Classification (OC) is a machine learning field that addresses classification tasks where the labels exhibit a natural order. Unlike nominal classification, which treats all classes as equally distinct, OC takes the ordinal relationship into account, producing more accurate and relevant results. This is particularly critical in applications where the magnitude of classification errors has implications. Despite this, OC problems are often tackled using nominal methods, leading to suboptimal solutions. Although decision trees are one of the most popular classification approaches, ordinal tree-based approaches have received less attention when compared to other classifiers. This work conducts an experimental study of tree-based methodologies specifically designed to capture ordinal relationships. A comprehensive survey of ordinal splitting criteria is provided, standardising the notations used in the literature for clarity. Three ordinal splitting criteria, Ordinal Gini (OGini), Weighted Information Gain (WIG), and Ranking Impurity (RI), are compared to the nominal counterparts of the first two (Gini and information gain), by incorporating them into a decision tree classifier. An extensive repository considering 45 publicly available OC datasets is presented, supporting © 2024, CC BY-NC-ND.