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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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
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