Option Predictive Clustering Trees for Hierarchical Multi-label Classification

Abstract

In this work, we address the task of hierarchical multi-label classification (HMLC). HMLC is a variant of classification, where a single example may belong to multiple classes at the same time and the classes are organized in the form of a hierarchy. Many practically relevant problems can be presented as a HMLC task, such as predicting gene function, habitat modelling, annotation of images and videos, etc. We propose to extend the predictive clustering trees for HMLC – a generalization of decision trees for HMLC – toward learning option predictive clustering trees (OPCTs) for HMLC. OPCTs address the myopia of the standard tree induction by considering alternative splits in the internal nodes of the tree. An option tree can also be regarded as a condensed representation of an ensemble. We evaluate OPCTs on 12 benchmark HMLC datasets from various domains. With the least restrictive parameter values, OPCTs are comparable to the state-of-the-art ensemble methods of bagging and random forest of PCTs. Moreover, OPCTs statistically significantly outperform PCTs.