Predictive Bi-clustering Trees for Hierarchical Multi-label Classification

Abstract

In the recent literature on multi-label classification, a lot of attention is given to methods that exploit label dependencies. Most of these methods assume that the dependencies are static over the entire instance space. In contrast, here we present an approach that dynamically adapts the label partitions in a multi-label decision tree learning context. In particular, we adapt the recently introduced predictive bi-clustering tree (PBCT) method towards multi-label classification tasks. This way, tree nodes can split the instance-label matrix both in a horizontal and a vertical way. We focus on hierarchical multi-label classification (HMC) tasks, and map the label hierarchy to a feature set over the label space. This feature set is exploited to infer vertical splits, which are regulated by a lookahead strategy in the tree building procedure. We evaluate our proposed method using benchmark datasets. Experiments demonstrate that our proposal (PBCT-HMC) obtained better or competitive results in comparison to its direct competitors, both in terms of predictive performance and model size. Compared to an HMC method that does not produce label partitions though, our method results in larger models on average, while still producing equally large or smaller models in one third of the datasets by creating suitable label partitions.