Abstract
We discuss the problem of classification using the first order hypotheses. This paper proposes an enhancement of classification based on the naive Bayesian scheme that is able to overcome the conditional independence assumption. Several experiments, involving some artificial and real-world, both propositional and relational domains, were conducted. The results indicate that the classification performance of propositional learners is reached when the richer first-order knowledge representation is not mandatory. This holds also in the domains where such representation is more convenient. Our framework can also benefit from the use of the hypotheses describing negative information. In such case, the classification becomes more noise resistant.
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References
Ali, K. M. and M. J. Pazzani (1993). Hydra. A noisetolerant relational concept learning algorithm. In Proceedings of the 13 th IJCAI, Chambery, France, pp. 1064–1070.
Cestnik, B. (1990). Estimating probabilities: A crucial task in machine learning. In Proceedings of European Conference on Artificial Intelligence, Stockholm, pp. 147–149.
Dolšak, B. and S. Muggleton (1992). The application of inductive logic programming to finite elements mesh design. In S. Muggleton (Ed.), Inductive Logic Programming. Academic Press.
Domingos, P. and M. J. Pazzani (1996). Beyond independence: Conditions for the optimality of the simple Bayesian classifier. In L. Saitta (Ed.), Proceedings of the Thirteenth International Conference on Machine Learning, Bari, Italy, pp. 105–112. Morgan Kaufman.
Džeroski, S. (1991). Handling noise in inductive logic programming. Master's thesis, University of Ljubljana, Faculty of electrical engineering and computer science, Ljubljana, Slovenia.
Kononenko, I. (1991). Semi-naive Bayesian classifier. In Y. Kodratoff (Ed.), Proceedings of European Working Session on Learning, Porto, pp. 206–219. Springer Verlag.
Kononenko, I., E. Šimec, and M. Robnik (1996). Overcoming the myopia of inductive learning algorithms with ReliefF. Journal of Applied Intelligence.
Lloyd, J. W. (1987). Foundations of Logic Programming (Second ed.). Berlin, Germany: Springer Verlag.
Muggleton, S. (1992). Inductive Logic Programming. London, England: Academic Press.
Muggleton, S. (1996). Stochastic logic programs. In L. De Raedt (Ed.), Advances in Inductive Logic Programming, pp. 254–264. Amsterdam, Netherlands: IOS Press.
Pompe, U. and I. Kononenko (1995a). Linear space induction in first order logic with Relief. In R. Kruse, R. Viertl, and G. Della Riccia (Eds.), CISM Lecture notes. Udine, Italy: Springer Verlag.
Pompe, U. and I. Kononenko (1995b). Naive bayesian classifier within ILP-R. In L. De Raedt (Ed.), Proceedings of the 5 th International Workshop on ILP, Leuven, Belgium, pp. 417–436. Katholieke Universiteit Leuven.
Quinlan, J. R. (1996). Learning first-order definitions of functions. Journal of Artificial Intelligence Research 5, 139–161.
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© 1997 Springer-Verlag Berlin Heidelberg
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Pompe, U., Kononenko, I. (1997). Probabilistic first-order classification. In: Lavrač, N., Džeroski, S. (eds) Inductive Logic Programming. ILP 1997. Lecture Notes in Computer Science, vol 1297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3540635149_52
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DOI: https://doi.org/10.1007/3540635149_52
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