Skip to main content

Pre-processing for noise detection in gene expression classification data


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.


  1. Aggarwal CC, Hinneburg A, Keim DA. On the surprising behavior of distance metrics in high dimensional space. In:Proceedings of the 8 th Int. Conf. on Database Theory, LNCS —vol. 1973; 2001; London. Springer-Verlag; 2001. p. 420–434.

    Chapter  Google Scholar 

  2. Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ. Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays. In:Proceedings of National Academy of Sciences of the United States of America; 1999. USA: The National Academy of Sciences; 1999. p. 6745–6750.

    Google Scholar 

  3. Barnett V, Lewis T.Outliers in statistical data. 3 ed. New York: Wiley Series in Probability & Statistics, John Wiley and Sons; 1994.

  4. Brown M, Grundy W, Lin D, Christianini N, Sugnet CM Jr., Haussler D.Support vector machine classification of microarray gene expression data. Santa Cruz, CA 95065: University of California; 1999. Technical Report UCSC-CRL-99-09.

    Google Scholar 

  5. Chien-Yu C. Detecting homogeneity in protein sequence clusters for automatic functional annotation and noise detection. In:Proceedings of the 5th Emerging Information Technology Conference; 2005; Taipei.

  6. Cohen WW. Fast effective rule induction. In:Proceedings of the 12th International Conference on Machine Learning; 1995. Tahoe City, CA: Morgan Kaufmann; 1995. p. 115–123.

    Google Scholar 

  7. Collobert R, Bengio S. SVMTorch: support vector machines for large-scale regression problems.The Journal of Machine Learning Research 2001; 1:143–160.

    Article  MathSciNet  Google Scholar 

  8. Corney DPA.Intelligent analysis of small data sets for food design London: Computer Science Department, London University College; 2002.

    Google Scholar 

  9. Cristianini N, Shawe-Taylor J.An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press; 2000.

    Google Scholar 

  10. Demsar J. Statistical comparisons of classifiers over multiple datasets.Journal of Machine Learning Research 2006; 7:1–30.

    MathSciNet  Google Scholar 

  11. Dudoit S, Fridlyand J, Speed TP.Comparison of discrimination methods for the classication of tumors using gene expression data. UC Berkeley: Department of Statistics; 2000. Technical Report 576.

    Google Scholar 

  12. Dunn OJ. Multiple comparisons among means.Journal of American Statistical Association 1961; 56(293):52–64.

    Article  MATH  MathSciNet  Google Scholar 

  13. Frank E, Witten IH.Data mining: practical machine learning tools and techniques. San Francisco: Morgan Kaufmann; 2005.

    MATH  Google Scholar 

  14. Friedman M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance.Journal of American Statistical Association 1937; 32(200):675–701.

    Article  Google Scholar 

  15. Golub TR, Tamayo P, Slonim D, Mesirow J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. In:Proceedings of National Academy of Sciences; 1999. USA: The National Academy of Sciences; 1999; 96(6):2907–2912.

    Google Scholar 

  16. He Z, Xu X, Deng S. Discovering cluster-based local outliers.Pattern Recognition Letters 2003; 24(9–10):1641–1650.

    Article  MATH  Google Scholar 

  17. Hodge V, Austin J. A survey of outlier detection methodologies.Artificial Intelligence Review 2004; 22(2):85–126.

    Article  MATH  Google Scholar 

  18. Hu J. Cancer outlier detection based on likelihood ratio test.Bioinformatics 2008; 24(19):2193–2199.

    Article  Google Scholar 

  19. Khoshgoftaar TM, Rebours P. Generating multiple noise elimination filters with the ensemble-partitioning filter. In:Proceedings of the IEEE International Conference on Information Reuse and Integration; 2004. p. 369–375.

  20. Knorr EM, Ng RT, Tucakov V. Distance-based outliers: algorithms and applications.The VLDB Journal 2000; 8(3–4):237–253.

    Article  Google Scholar 

  21. Lavrac N, Gamberger D. Saturation filtering for noise and outlier detection. In:Proceedings of the Workshop in Active Learning, Database Sampling, Experimental Design: Views on Instance Selection, 12th European Conference on Machine Learning; 2001. p. 1–4.

  22. Lorena AC, Carvalho ACPLF. Evaluation of noise reduction techniques in the splice junction recognition problem.Genetics and Molecular Biology 2004; 27(4):665–672.

    Article  Google Scholar 

  23. Libralon GL, Lorena AC, Carvalho ACPLF. Ensembles of pre processing techniques for noise detection in gene expression data. In:Proceedings of 15th International Conference on Neural Information Processing of the Asia-Pacific Neural Network Assembly; ICONIP2008; Auckland, New Zealand. 2008. p. 1–10.

  24. Liu W. Outlier detection for microarray data. In:Proceedings of the 2 nd International Conference on Bioinformatics and Biomedical Engineering — ICBBE; 2008; Shanghai. p. 585–586.

  25. Mitchell T.Machine learning. USA: McGraw Hill; 1997.

    MATH  Google Scholar 

  26. Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data.Machine Learning 2003; 52(1–2):91–118.

    Article  MATH  Google Scholar 

  27. Quinlan JR.C4.5: programs for machine learning. San Francisco, CA: Morgan Kaufmann; 1993.

    Google Scholar 

  28. Schlkopf B.SVMs: a practical consequence of learning theory.IEEE Intelligent Systems 1998; 13(4):36–40.

    Google Scholar 

  29. Stanfill C, Waltz D. Toward memory-based reasoning.Communications of the ACM 1986; 29(12):1213–1228.

    Article  Google Scholar 

  30. Tang J, Chen Z, Fu AW, Cheung D. A robust outlier detection scheme in large data sets. In:Proceedings of the 6th Pacific-Asia Conference on Knowledge Discovery and Data Mining; 2002; Taipei. p. 535–548.

  31. Tomek I. Two modifications of CNN.IEEE Transactions on Systems, Man and Cybernetics 1976; 7(11):769–772.

    MathSciNet  Google Scholar 

  32. 32. Van Hulse JD, Khoshgoftaar TM, Huang H. The pairwise attribute noise detection algorithm.Knowledge and Information Systems 2007; 11(2):171–190.

    Article  Google Scholar 

  33. Vapnik VN.The nature of statistical learning theory. 2 ed. Berlim: Springer-Verlag; 1995.

    MATH  Google Scholar 

  34. Verbaeten S, Assche AV. Ensemble methods for noise elimination in classification problems. In:Proceedings of the 4th International Workshop on Multiple Classifier Systems; 2003. Berlim: Springer; 2003. p. 317–325.

    Google Scholar 

  35. Wilson DR, Martinez TR. Reduction techniques for instance-based learning algorithms.Machine Learning 2000; 38(3):257–286.

    Article  MATH  Google Scholar 

  36. Wilson DR, Martinez TR. Improved heterogeneous distance functions.Journal of Artificial Intelligence Research 1997; 6(1):1–34.

    MATH  MathSciNet  Google Scholar 

  37. Wilson DL. Asymptotic properties of nearest neighbor rules using edited data.IEEE Transactions on Systems, Man and Cybernetics 1972; 2(3):408–421.

    Article  MATH  Google Scholar 

  38. Yeoh EJ, Ross ME, Shurtle SA, Williams WK, Patel D, Mahfouz R. et al. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling.Cancer Cell 2002; 1(2):133–143.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Libralon, G.L., de Carvalho, A.C.P.d.L.F. & Lorena, A.C. Pre-processing for noise detection in gene expression classification data. J Braz Comp Soc 15, 3–11 (2009).

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: