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Filtering Noisy Continuous Labeled Examples

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 2527)

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·logN+NA 2) 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.

Keywords

  • Forward Table
  • Machine Learn System
  • Embed Algorithm
  • Adequate Rule
  • Machine Learning Library

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The research reported in this paper has been supported in part under MCyT and Feder grant TIC2001-3579

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© 2002 Springer-Verlag Berlin Heidelberg

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Ramón Quevedo Pérez, J., Dolores García, M., Montañés, E. (2002). Filtering Noisy Continuous Labeled Examples. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_6

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  • DOI: https://doi.org/10.1007/3-540-36131-6_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00131-7

  • Online ISBN: 978-3-540-36131-2

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