Filtering Noisy Continuous Labeled Examples

Authors:
José Ramón Quevedo Pérez;María Dolores García;Elena Montañés
Affiliations:
-;-;-
Venue:
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Year:
2002

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Abstract

It is common in Machine Learning where rules are learned from examples that some of them could not be informative, otherwise they could be irrelevant or noisy. This type of examples makes the Machine Learning Systems produce not adequate rules. In this paper we present an algorithm that filters noisy continuous labeled examples, whose computational cost is O(N驴logN+NA2) for N examples and A attributes. Besides, it is shown experimentally to be better than the embedded algorithms of the state-of-the art of the Machine Learning Systems.