Fully associative ensemble learning for Hierarchical multi-label classification

In contrast to traditional flat classification problems (e.g., binary or multi-class classification), Hierarchical Multi-label Classification (HMC) takes into account the structural information embedded in the class hierarchy. In this paper, we propose a local hierarchical ensemble framework, Fully Associative Ensemble Learning (FAEL). We model the relationship between each node's global prediction and the local predictions of all the nodes as a multi-variable regression problem. The simplest version of our model leads to a ridge regression problem. It can be extended using the kernel trick, which explores the complex correlation between global and local prediction. In addition, we introduce a binary constraint model to restrict the optimal weight matrix learning. The proposed models have been applied to image annotation and gene function prediction datasets. The experimental results indicate that our models achieve better performance when compared with other baseline methods.