Learning Hierarchical Multi-label Classification Trees from Network Data

Abstract

We present an algorithm for hierarchical multi-label classification (HMC) in a network context. It is able to classify instances that may belong to multiple classes at the same time and consider the hierarchical organization of the classes. It assumes that the instances are placed in a network and uses information on the network connections during the learning of the predictive model. Many real world prediction problems have classes that are organized hierarchically and instances that can have pairwise connections. One example is web document classification, where topics (classes) are typically organized into a hierarchy and documents are connected by hyperlinks. Another example, which is considered in this paper, is gene/protein function prediction, where genes/proteins are connected and form protein-to-protein interaction (PPI) networks. Network datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. Combining the hierarchical multi-label classification task with network prediction is thus not trivial and requires the introduction of the new concept of network autocorrelation for HMC. The proposed algorithm is able to profitably exploit network autocorrelation when learning a tree-based prediction model for HMC. The learned model is in the form of a Predictive Clustering Tree (PCT) and predicts multiple (hierarchically organized) labels at the leaves. Experiments show the effectiveness of the proposed approach for different problems of gene function prediction, considering different PPI networks. The results show that different networks introduce different benefits in different problems of gene function prediction.