Open Access

Pre-processing for noise detection in gene expression classification data

  • Giampaolo Luiz Libralon1,
  • Andr√© Carlos Ponce de Leon Ferreira de Carvalho1 and
  • Ana Carolina Lorena2
Journal of the Brazilian Computer Society15:BF03192573

DOI: 10.1007/BF03192573

Received: 27 August 2008

Accepted: 1 March 2009


Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.