Abstract
Hierarchical Multi-label Classification (HMC) is the task of assigning a set of classes to a single instance with the peculiarity that the classes are ordered in a predefined structure. We propose a novel HMC method for tree and Directed Acyclic Graphs (DAG) hierarchies. Using the combined predictions of locals classifiers and a weighting scheme according to the level in the hierarchy, we select the “best” single path for tree hierarchies, and multiple paths for DAG hierarchies. We developed a method that returns paths from the root down to a leaf node (Mandatory Leaf Node Prediction or MLNP) and an extension for Non Mandatory Leaf Node Prediction (NMLNP). For NMLNP we compared several pruning approaches varying the pruning direction, pruning time and pruning condition. Additionally, we propose a new evaluation metric for hierarchical classifiers, that avoids the bias of current measures which favor conservative approaches when using NMLNP. The proposed approach was experimentally evaluated with 10 tree and 8 DAG hierarchical datasets in the domain of protein function prediction. We concluded that our method works better for deep, DAG hierarchies and in general NMLNP improves MLNP.
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References
Alaydie, N., Reddy, C.K., Fotouhi, F.: Exploiting label dependency for hierarchical multi-label classification. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part I. LNCS, vol. 7301, pp. 294–305. Springer, Heidelberg (2012)
Ashburner, M., Ball, C.A., Blake, J.A.: Gene Ontology: Tool for the unification of biology. Nature Genetics 25, 25–29 (2000)
Bi, W., Kwok, J.T.: Multilabel classification on tree- and dag-structured hierarchies. In: Proc. of the 28th Inter. Conf. on ML (ICML), pp. 17–24. Omnipress (2011)
Bi, W., Kwok, J.T.: Hierarchical multilabel classification with minimum bayes risk. In: IEEE Intl. Conf, on Data Mining (ICDM), pp. 101–110. IEEE Computer Society (2012)
Cerri, R., Barros, R.C., de Carvalho, A.C.P.L.F.: Hierarchical multi-label classification using local neural networks. J. Comput. System Sci. 1, 1–18 (2013)
Dekel, O., Keshet, J., Singer, Y.: An online algorithm for hierarchical phoneme classification. In: Bengio, S., Bourlard, H. (eds.) MLMI 2004. LNCS, vol. 3361, pp. 146–158. Springer, Heidelberg (2005)
Hernandez, J.N., Sucar, L.E., Morales, E.F.: A hybrid global-local approach for hierarchical classification. In: Boonthum-Denecke, C., Youngblood, G.M. (eds.) Proceedings FLAIRS Conference, pp. 432–437. AAAI Press (2013)
Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. In: Proceedings of the 14th International Conference on Machine Learning, ICML 1997, pp. 170–178. Morgan Kaufmann Publishers Inc., San Francisco (1997)
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Machine Learning, 254–269 (2011)
Rousu, J., Saunders, C., Szedmak, S., Shawe-Taylor, J.: Kernel-based learning of hierarchical multilabel classification models. J Mach. Learn. Res. 7, 1601–1626 (2006)
Ruepp, A., Zollner, A., Maier, D., Albermann, K., Hani, J., Mokrejs, M., Tetko, I., Güldener, U., Mannhaupt, G., Münsterkötter, M., Mewes, H.W.: The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Research 32(18), 5539–5545 (2004)
Silla Jr., C.N., Freitas, A.A.: Novel top-down approaches for hierarchical classification and their application to automatic music genre classification. In: IEEE Inter. Conf. on Systems, Man, and Cybernetics, pp. 3499–3504 (2009)
Silla Jr., C.N., Freitas, A.A.: A global-model naive bayes approach to the hierarchical prediction of protein functions. In: Proceedings of the 2009 9th IEEE International Conference on Data Mining, ICDM 2009, pp. 992–997. IEEE Computer Society, Washington, DC (2009)
Tsoumakas, G., Katakis, I.: Multi-Label Classification: An Overview. Int. J. Data Warehouse. Min. 3, 1–13 (2007)
Vens, C., Struyf, J., Schietgat, L., Džeroski, S., Blockeel, H.: Decision trees for hierarchical multi-label classification. Machine Learning 73(2), 185–214 (2008)
Zaragoza, J.H., Sucar, L.E., Morales, E.F., Bielza, C., Larrañaga, P.: Bayesian Chain Classifiers for Multidimensional Classification. Comp. Intelligence, 2192–2197 (2011)
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Ramírez-Corona, M., Sucar, L.E., Morales, E.F. (2014). Multi-label Classification for Tree and Directed Acyclic Graphs Hierarchies. In: van der Gaag, L.C., Feelders, A.J. (eds) Probabilistic Graphical Models. PGM 2014. Lecture Notes in Computer Science(), vol 8754. Springer, Cham. https://doi.org/10.1007/978-3-319-11433-0_27
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DOI: https://doi.org/10.1007/978-3-319-11433-0_27
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