Hierarchical Multi-label Classification using Fully Associative Ensemble Learning. (October 2017) - Zhang, L.; Shah, S.K.; Kakadiaris, I.A.
Highlights: Developing a local hierarchical ensemble framework for Hierarchical Multi-label Classification (HMC), in which all the structural relationships in the class hierarchy are used to obtain global prediction. Introducing empirical loss minimization into HMC, so that the learned model can capture the most useful information from historical data. Proposing sparse, kernel, and binary constraint HMC models. Abstract: Traditional flat classification methods ( e.g., binary or multi-class classification) neglect the structural information between different classes. In contrast, Hierarchical Multi-label Classification (HMC) considers the structural information embedded in the class hierarchy, and uses it to improve classification performance. In this paper, we propose a local hierarchical ensemble framework for HMC, Fully Associative Ensemble Learning (FAEL). We model the relationship between each class node's global prediction and the local predictions of all the class nodes as a multi-variable regression problem with Frobenius norm or l 1 norm regularization. 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 with tree structured class hierarchy and large scale visual recognition dataset with Direct Acyclic Graph (DAG) structured class hierarchy. The experimental results indicate that our models achieve better performance when compared with other baseline methods.