Evaluation of Different Data-Derived Label Hierarchies in Multi-label Classification

Abstract

Motivated by an increasing number of new applications, the research community is devoting an increasing amount of attention to the task of multi-label classification (MLC). Many different approaches to solving multi-label classification problems have been recently developed. Recent empirical studies have comprehensively evaluated many of these approaches on many datasets using different evaluation measures. The studies have indicated that the predictive performance and efficiency of the approaches could be improved by using data derived (artificial) hierarchies, in the learning and prediction phases. In this paper, we compare different clustering algorithms for constructing the label hierarchies (in a data-driven manner), in multi-label classification. We consider flat label sets and construct the label hierarchies from the label sets that appear in the annotations of the training data by using four different clustering algorithms (balanced $$k$$-means, agglomerative clustering with single and complete linkage and predictive clustering trees). The hierarchies are then used in conjunction with global hierarchical multi-label classification (HMC) approaches. The results from the statistical and experimental evaluation reveal that the data-derived label hierarchies used in conjunction with global HMC methods greatly improve the performance of MLC methods. Additionally, multi-branch hierarchies appear much more suitable for the global HMC approaches as compared to the binary hierarchies.