GML_DT: A Novel Graded Multi-label Decision Tree Classifier

Abstract

The goal of Graded Multi-label Classification (GMLC) is to assign a degree of membership or relevance of a class label to each data point. As opposed to multi-label classification tasks which can only predict whether a class label is relevant or not. The graded multi-label setting generalizes the multi-label paradigm to allow a prediction on a gradual scale. This is in agreement with practical real-world applications where the labels differ in matter of level relevance. In this paper, we propose a novel decision tree classifier (GML_DT) that is adapted to the graded multi-label setting. It fully models the label dependencies, which sets it apart from the transformation-based approaches in the literature, and increases its performance. Furthermore, our approach yields comprehensive and interpretable rules that efficiently predict all the degrees of memberships of the class labels at once. To demonstrate the model’s effectiveness, we tested it on real-world graded multi-label datasets and compared it against a baseline transformation-based decision tree classifier. To assess its predictive performance, we conducted an experimental study with different evaluation metrics from the literature. Analysis of the results shows that our approach has a clear advantage across the utilized performance measures.