Abstract
Local methods have significant advantages when the probability measure defined on the space of symbolic objects for each class is very complex, but can still be described by a collection of less complex local approximations. We propose a technique of local bagging of decision stumps. We performed a comparison with other well known combining methods using the same base learner, on standard benchmark datasets and the accuracy of the proposed technique was greater in most cases.
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References
Aha, D.: Lazy Learning. Kluwer Academic Publishers, Dordrecht (1997)
Atkeson, C.G., Moore, A.W., Schaal, S.: Locally weighted learning for control. Artificial Intelligence Review 11, 75–113 (1997)
Atkeson, C.G., Moore, A.W., Schaal, S.: Locally weighted learning. Artificial Intelligence Review 11, 11–73 (1997)
Blake, C., Merz, C.: UCI Repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA (1998), http://www.ics.uci.edu/mlearn/MLRepository.html
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning 36, 525–536 (1999)
Brighton, H., Mellish, C.: Advances in Instance Selection for Instance-Based Learning Algorithms. Data Mining and Knowledge Discovery 6, 153–172 (2002)
Bottou, L., Vapnik, V.: Local learning algorithm. Neural Computation 4(6), 888–901 (1992)
Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)
Cohen, S., Intrator, N.: Automatic model selection in a hybrid perceptron/Radial network. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 349–358. Springer, Heidelberg (2001)
John, C., Trigg, L.: An Instance- based Learner Using an Entropic Distance Measure. In: Proc. of the 12th International Conference on ML, pp. 108–114 (1995)
Frank, E., Hall, M., Pfahringer, B.: Locally weighted naive Bayes. In: Proc. of the 19th Conference on Uncertainty in Artificial Intelligence, Acapulco, Mexico. Morgan Kaufmann, San Francisco (2003)
Freund, Y., Schapire, R.: Experiments with a New Boosting Algorithm. In: Proc. ICML 1996, pp. 148–156 (1996)
Friedman, J.H., Hastie, T., Tibshirani, R.: Additive logistic regression: A statistical view of boosting. The Annals of Statistics 28, 337–374 (2000)
Iba, W., Langley, P.: Induction of one-level decision trees. In: Proc. of the Ninth International Machine Learning Conference. Morgan Kaufmann, Aberdeen (1992)
Kleinberg, E.M.: A mathematically rigorous foundation for supervised learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 67–76. Springer, Heidelberg (2000)
Nadeau, C., Bengio, Y.: Inference for the Generalization Error. Machine Learning 52, 239–281 (2003)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
Wilson, D., Martinez, T.: Reduction Techniques for Instance-Based Learning Algorithms. Machine Learning 38, 257–286 (2000)
Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Mateo (2000)
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Kotsiantis, S.B., Tsekouras, G.E., Pintelas, P.E. (2005). Local Bagging of Decision Stumps. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_57
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DOI: https://doi.org/10.1007/11504894_57
Publisher Name: Springer, Berlin, Heidelberg
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