Abstract
In this paper an approach to multi-class (as opposed to multi-label) classification is proposed. The idea is that a more effective classification can be produced if a coarse-grain classification (directed at groups of classes) is first conducted followed by increasingly more fine-grain classifications. A framework is proposed whereby this scheme can be realised in the form of a classification hierarchy. The main challenge is how best to create class groupings with respect to the labels nearer the root of the hierarchy. Three different techniques, based on the concepts of clustering and splitting, are proposed. Experimental results show that the proposed mechanism can improve classification performance in terms of average accuracy and average AUC in the context of some data sets.
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References
Bache, K., Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning pp. 105–139 (1999).
Breiman, L.: Bagging predictors. Machine Learning (2), 123–140 (1996).
Breiman, L.: Random forests. In. Machine Learning, pp. 5–32 (2001).
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth and Brooks, Monterey, CA (1984).
Coenen, F.: The LUCS-KDD discretised/normalised arm and carm data library (2003). http://www.csc.liv.ac.uk/frans/KDD/Software/LUCS_KDD_DN
Coenen, F., Leng, P.: The effect of threshold values on association rule based classification accuracy. Journal of, Data and Knowledge Engineering (2), pp 345–360 (2007).
Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. JAIR (1995).
Dunham, M.H.: Data Mining:Introductory and Advanced Topics. Prentice Hall (2003).
Freund, Y., Schapire, R., Abe, N.: A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence (5), 771–80 (1999).
Jiawei, H., Micheline, K., Jian, P.: Data Mining: Concepts and Techniques. Morgan Kaufmann (2011).
Leonard, T., Hsu, J.S.: Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers. Cambridge University Press (2001).
Machov, K., Bark, F., Bednr, P.: A bagging method using decision trees in the role of base classifiers (2006).
Quinlan, J.R.: Induction of decision trees. Machine Learning (1), 81–106 (1986).
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993).
Rifkin, R.M., Klautau, A.: In defense of one-vs-all classification. Journal of Machine Learning Research pp. 101–141 (2004).
Tax, D.M.J., Duin, R.P.W.: Using two-class classifiers for multiclass classification. In: ICPR (2), pp. 124–127 (2002).
Vapnik, V.N.: The Nature of Statistical Learning Theory. Statistics for Engineering and Information Science. Springer (2000).
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Alshdaifat, E., Coenen, F., Dures, K. (2013). Hierarchical Single Label Classification: An Alternative Approach. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXX. SGAI 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-02621-3_3
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DOI: https://doi.org/10.1007/978-3-319-02621-3_3
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