Abstract
In the era of semantic web and big data, the need for machine learning algorithms able to exploit domain ontologies is undeniable. In the past, two divergent research lines were followed, but with background knowledge represented through domain ontologies, is now possible to develop new ontology-driven learning algorithms. In this paper, we propose a method that adds domain knowledge, represented in OWL 2, to a purely statistical decision tree learner. The new algorithm tries to find the best attributes to test in the decision tree, considering both existing attributes and new ones that can be inferred from the ontology. By exploring the set of axioms in the ontology, the algorithm is then able to determine in run-time the best level of abstraction for each attribute, infer new attributes and decide the ones to be used in the tree. Our experimental results show that our method produces smaller and more accurate trees even on data sets where all features are concrete, but specially on those where some features are only specified at higher levels of abstraction. We also show that our method performs substantially better than traditional decision tree classifiers in cases where only a small number of labeled instances are available.
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References
Altman, N.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)
Antunes, C.: D2PM: domain driven pattern mining. Technical report, Project report, Technical Report 1530, IST, Lisboa (2011)
Bache, K., Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
Blockeel, H., De Raedt, L.: Top-down induction of first-order logical decision trees. Artif. Intell. 101(1), 285–297 (1998)
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM (1992)
Bramer, M.: Using J-pruning to reduce overfitting in classification trees. Knowl.-Based Syst. 15(5), 301–308 (2002)
Domingos, P., Kok, S., Poon, H., Richardson, M., Singla, P.: Unifying logical and statistical AI. In: AAAI (2006)
Dzeroski, S., Jacobs, N., Molina, M., Moure, C., Muggleton, S., Laer, W.V.: Detecting traffic problems with ILP. In: Page, D.L. (ed.) ILP 1998. LNCS, vol. 1446, pp. 281–290. Springer, Heidelberg (1998)
Hawkins, D.M.: The problem of overfitting. J. Chem. Inf. Comput. Sci. 44(1), 1–12 (2004)
Kazakov, Y., Krtzsch, M., Simančík, F.: The incredible ELK. J. Autom. Reasoning 53(1), 1–61 (2014). http://dx.doi.org/10.1007/s10817-013-9296-3
Lincoff, G., Nehring, C.: National Audubon Society Field Guide to North American Mushrooms. Knopf, New York (1997)
Maimon, O., Rokach, L. (eds.): Data Mining and Knowledge Discovery Handbook, 2nd edn. Springer, New York (2010)
Motik, B., Patel-Schneider, P.F., Parsia, B., Bock, C., Fokoue, A., Haase, P., Hoekstra, R., Horrocks, I., Ruttenberg, A., Sattler, U., et al.: Owl 2 web ontology language: structural specification and functional-style syntax. W3C recommendation 27, 17 (2009)
Muggleton, S., De Raedt, L., Poole, D., Bratko, I., Flach, P., Inoue, K., Srinivasan, A.: ILP turns 20. Mach. Learn. 86(1), 3–23 (2012)
Núñez, M.: The use of background knowledge in decision tree induction. Mach. Learn. 6(3), 231–250 (1991)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Quinlan, J.R., Cameron-Jones, R.M.: Foil: a midterm report. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 1–20. Springer, Heidelberg (1993)
Quinlan, J.R.: C4.5: Programs for Machine Learning, vol. 1. Morgan Kaufmann, San Francisco (1993)
Roberts, S., Jacobs, N., Muggleton, S., Broughton, J., et al.: A comparison of ILP and propositional systems on propositional traffic data. In: Page, D.L. (ed.) ILP 1998. LNCS, vol. 1446, pp. 291–299. Springer, Heidelberg (1998)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Cogn. Model. 1, 213 (2002)
Srinivasan, A., King, R.D., Muggleton, S.: The role of background knowledge: using a problem from chemistry to examine the performance of an ILP program. Trans. Knowl. Data Eng. (1999)
White, A.P., Liu, W.Z.: Technical note: Bias in information-based measures in decision tree induction. Mach. Learn. 15(3), 321–329 (1994)
Zhang, J., Kang, D.K., Silvescu, A., Honavar, V.: Learning accurate and concise naïve bayes classifiers from attribute value taxonomies and data. Knowl. Inf. Syst. 9(2), 157–179 (2006)
Zhang, J., Silvescu, A., Honavar, V.G.: Ontology-driven induction of decision trees at multiple levels of abstraction. In: Koenig, S., Holte, R. (eds.) SARA 2002. LNCS (LNAI), vol. 2371, pp. 316–323. Springer, Heidelberg (2002)
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Vieira, J., Antunes, C. (2014). Decision Tree Learner in the Presence of Domain Knowledge. In: Zhao, D., Du, J., Wang, H., Wang, P., Ji, D., Pan, J. (eds) The Semantic Web and Web Science. CSWS 2014. Communications in Computer and Information Science, vol 480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45495-4_4
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DOI: https://doi.org/10.1007/978-3-662-45495-4_4
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