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Use of Classification Algorithms in Noise Detection and Elimination

  • André L. B. Miranda
  • Luís Paulo F. Garcia
  • André C. P. L. F. Carvalho
  • Ana C. Lorena
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5572)

Abstract

Data sets in Bioinformatics usually present a high level of noise. Various processes involved in biological data collection and preparation may be responsible for the introduction of this noise, such as the imprecision inherent to laboratory experiments generating these data. Using noisy data in the induction of classifiers through Machine Learning techniques may harm the classifiers prediction performance. Therefore, the predictions of these classifiers may be used for guiding noise detection and removal. This work compares three approaches for the elimination of noisy data from Bioinformatics data sets using Machine Learning classifiers: the first is based in the removal of the detected noisy examples, the second tries to reclassify these data and the third technique, named hybrid, unifies the previous approaches.

Keywords

Noise Machine Learning Gene Expression and Classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • André L. B. Miranda
    • 1
  • Luís Paulo F. Garcia
    • 1
  • André C. P. L. F. Carvalho
    • 1
  • Ana C. Lorena
    • 2
  1. 1.Instituto de Ciências Matemáticas e ComputaçãoUniversidade de São Paulo USPSão CarlosBrazil
  2. 2.Centro de Matemática, Computação e CogniçãoUniversidade Federal do ABC UFABCSanto AndréBrazil

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