Affiliations: [a] Department of Computer Engineering, Chulalongkorn University, Bangkok, Thailand | [b] Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA | [c] Hydro Informatics Division, Hydro and Argo Informatics Institute, Bangkok, Thailand
Correspondence:
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Corresponding author: Peerapon Vateekul, Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand. Tel.: +66 2218 6989; E-mail: peerapon.v@chula.ac.th.
Abstract: Hierarchical multi-label classification is a relatively new research topic in the field of classifier induction. What distinguishes it from earlier tasks is that it allows each example to belong to two or more classes at the same time, and by assuming that the classes are mutually related by generalization/specialization operators. The paper first investigates the problem of performance evaluation in these domains. After this, it proposes a new induction system, HR-SVM, built around support vector machines. In our experiments, we demonstrate that this system's performance compares favorably with that earlier attempts, and then we proceed to an investigation of how HR-SVM's individual modules contribute to the overall system's behavior. As a testbed, we use a set of benchmark domains from the field of gene-function prediction.
Keywords: Hierarchical multi-label classification, support vector machines, gene-function prediction
