Ensemble Methods for Noise Elimination in Classification Problems

  • Sofie Verbaeten
  • Anneleen Van Assche
Conference paper

DOI: 10.1007/3-540-44938-8_32

Volume 2709 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Verbaeten S., Van Assche A. (2003) Ensemble Methods for Noise Elimination in Classification Problems. In: Windeatt T., Roli F. (eds) Multiple Classifier Systems. MCS 2003. Lecture Notes in Computer Science, vol 2709. Springer, Berlin, Heidelberg

Abstract

Ensemble methods combine a set of classifiers to construct a new classifier that is (often) more accurate than any of its component classifiers. In this paper, we use ensemble methods to identify noisy training examples. More precisely, we consider the problem of mislabeled training examples in classification tasks, and address this problem by pre-processing the training set, i.e. by identifying and removing outliers from the training set. We study a number of filter techniques that are based on well-known ensemble methods like cross-validated committees, bagging and boosting. We evaluate these techniques in an Inductive Logic Programming setting and use a first order decision tree algorithm to construct the ensembles.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Sofie Verbaeten
    • 1
  • Anneleen Van Assche
    • 1
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenHeverleeBelgium