Abstract
We investigate here concept learning from incomplete examples, denoted here as ambiguous. We start from the learning from interpretations setting introduced by L. De Raedt and then follow the informal ideas presented by H. Hirsh to extend the Version space paradigm to incomplete data: a hypothesis has to be compatible with all pieces of information provided regarding the examples. We propose and experiment an algorithm that given a set of ambiguous examples, learn a concept as an existential monotone DNF. We show that 1) boolean concepts can be learned, even with very high incompleteness level as long as enough information is provided, and 2) monotone, non monotone DNF (i.e. including negative literals), and attribute-value hypotheses can be learned that way, using an appropriate background knowledge. We also show that a clever implementation, based on a multi-table representation is necessary to apply the method with high levels of incompleteness.
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References
DeRaedt, L.: Logical settings for concept-learning. Artif. Intell. 95(1), 187–201 (1997)
Mitchell, T.M.: Generalization as search. Artif. Intell. 18(2), 203–226 (1982)
Hirsh, H.: Generalizing version spaces. Mach. Learn. 17(1), 5–46 (1994)
Kakas, A.C., Riguzzi, F.: Abductive concept learning. New Generation Computing 18(3), 243–294 (2000)
Alphonse, É.: Macro-operators revisited in inductive logic programming. In: Camacho, R., King, R., Srinivasan, A. (eds.) ILP 2004. LNCS, vol. 3194, pp. 8–25. Springer, Heidelberg (2004)
Khardon, R.: Learning horn expressions with logan-h. In: ICML 2000: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 471–478. Morgan Kaufmann, San Francisco (2000)
VanLaer, W., DeRaedt, L., Dzeroski, S.: On multi-class problems and discretization in inductive logic programming. In: Raś, Z.W., Skowron, A. (eds.) ISMIS 1997. LNCS, vol. 1325, pp. 277–286. Springer, Heidelberg (1997)
Muggleton, S.: Inverse entailment and Progol. New Generation Computing 13(3-4), 245–286 (1995)
Wielemaker, J.: An overview of the SWI-Prolog programming environment. In: Mesnard, F., Serebenik, A. (eds.) Proceedings of the 13th International Workshop on Logic Programming Environments, Heverlee, Belgium, Katholieke Universiteit Leuven, December 2003, pp. 1–16, CW 371. Katholieke Universiteit Leuven (2003)
Schuurmans, D., Greiner, R.: Learning to classify incomplete examples. In: Computational Learning Theory and Natural Learning Systems: Addressing Real World Tasks, pp. 87–105. MIT Press, Cambridge (1997)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)
Saar-Tsechansky, M., Provost, F.: Handling missing values when applying classification models. Journal of machine learning research 8, 1623–1657 (2007)
Lim, T.S., Loh, W.Y., Shih, Y.S.: A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning 40(3), 203–228 (2000)
Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1-2), 31–71 (1997)
Zucker, J.D., Ganascia, J.G.: Learning structurally indeterminate clauses. In: Page, D.L. (ed.) ILP 1998. LNCS, vol. 1446, pp. 235–244. Springer, Heidelberg (1998)
Alphonse, É., Rouveirol, C.: Lazy propositionalization for relational learning. In: Horn, W. (ed.) Proc. of ECAI 2000, pp. 256–260. IOS Press, Amsterdam (2000)
Sebag, M., Rouveirol, C.: Resource-bounded relational reasoning: Induction and deduction through stochastic matching. Machine Learning Journal 38, 43–65 (2000)
Dick, U., Haider, P., Scheffer, T.: Learning from incomplete data with infinite imputations. In: ICML 2008, pp. 232–239. ACM, New York (2008)
Liu, W.Z., White, A.P., Thompson, S.G., Bramer, M.A.: Techniques for dealing with missing values in classification. In: Liu, X., Cohen, P.R., Berthold, M.R. (eds.) IDA 1997. LNCS, vol. 1280, pp. 527–536. Springer, Heidelberg (1997)
Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Dimopoulos, Y., Kakas, A.: Abduction and inductive learning. In: De Raedt, L. (ed.) Advances in Inductive Logic Programming, pp. 144–171. IOS Press, Amsterdam (1996)
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Bouthinon, D., Soldano, H., Ventos, V. (2009). Concept Learning from (Very) Ambiguous Examples. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_35
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DOI: https://doi.org/10.1007/978-3-642-03070-3_35
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