Hierarchical Multi-label Classification Problems: An LCS Approach
Traditional classification tasks deal with assigning instances to a single label. However, some real world databases classes are structured in a hierarchy and its instances can have their classes associated with two or more paths in the hierarchical structure. In this case, such situations are referred as hierarchical multi-label classification problems. The purpose of this paper is to explore the concept of hierarchical multi-label classification problems and present a solution based on Learning Classifier Systems (LCS) to solve this kind of problem. The Hierarchical Learning Classifier System Multi-label (HLCS-Multi) proposed, presents a comprehensive solution to hierarchical multi-label classification problems building a global classifier to predict all classes in the application domain.
KeywordsHierarchical Multi-label Classification Problems Learning Classifier Systems Protein Function
Unable to display preview. Download preview PDF.
- 1.Vateekul, P.: Hierarchical Multi-Label Classification: Going Beyond Generalization Trees. Open Access Dissertations, Paper 723 (2012)Google Scholar
- 2.Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1992)Google Scholar
- 3.Urbanowicz, R.J., Moore, J.H.: Learning classifier systems: a complete introduction, review, and roadmap. Journal Artif. Evol. App., 1:1–1:25 (2009)Google Scholar
- 6.Vens, C., Struyf, J., Schietgat, L., Džeroski, S., Blockeel, H.: Decision trees for hierarchical multi-label classification. Mach. Learn. 73(2), 185–214 (2008)Google Scholar
- 7.Kiritchenko, S., Matwin, S., Fazel, A.F.: Functional Annotation of Genes Using Hierarchical Text Categorization. In: Proceedings of BioLINK SIG: Linking Literature, Information and Knowledge for Biology (2005)Google Scholar