Use of classification algorithms in noise detection and elimination

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5572 LNAI, Issue , 2009, Pages 417-424

  Miranda, André L B ^a   Garcia, Luís Paulo F ^a   Carvalho, André C P L F ^a   Lorena, Ana C ^b  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b UNIVERSIDADE FEDERAL DO ABC   (Brazil)

Author keywords

Gene Expression and Classification; Machine Learning; Noise

Indexed keywords

BIOINFORMATICS DATA; BIOLOGICAL DATA; CLASSIFICATION ALGORITHM; DATA SETS; LABORATORY EXPERIMENTS; MACHINE LEARNING; MACHINE LEARNING TECHNIQUES; NOISE; NOISE DETECTION; NOISY DATA; PREDICTION PERFORMANCE;

ARTIFICIAL INTELLIGENCE; BIOACTIVITY; CLASSIFIERS; EDUCATION; GENE EXPRESSION; LEARNING ALGORITHMS; ROBOT LEARNING;

BIOINFORMATICS;

EID: 70350650320     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02319-4_50     Document Type: Conference Paper

References (14)

1. Zhu, X., Wu, X.: Class noise vs. Attribute noise: A quantitative study of their impacts. Artificial Intelligence Review 22(3), 177-210 (2004)
2. Van Hulse, J.D., Khoshgoftaar, T.M., Huang, H.: The pairwise attibrute noise detection algorithm. Knowl. Inf. Syst. 11(2), 171-190 (2007)
3. Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
4. Noble, W.S.: Kernel Methods in Computational Biology. In: Support vector machines applications in computational biology, vol. 3, pp. 71-92. MIT Press, Cambridge (2004)
5. Haykin, S.: Neural Network - A Compreensive foundation, 2nd edn. Prentice-Hall, New Jersey (1999)
6. Breiman, L., Friedman, F., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth (1984)
7. Verbaeten, S., Assche, A.V.: Ensemble Methods for noise elimination in Classification problems. In: Windeatt, T., Roli, F. (eds.) MCS 2003. LNCS, vol. 2709, pp. 317-325. Springer, Heidelberg (2003)
8. Hodge, V., Austin, J.: A survey of outlier detection methodologies. Artificial Intelligence Review 22, 85-126 (2004)
9. Demsar, J.: Statistical Comparisons of Classifiers over Multiple Data Sets. The Journal of Machine Learning Research 7, 1-30 (2006)
10. Mack, D.H., Tom, E.Y., Mahadev, M., Dong, H., Mittman, M., Dee, S., Levine, A.J., Gingeras, T.R., Lockhart, D.J.: Biology of Tumors. In: Mihich, K., Croce, C. (eds.), pp. 123-131. Plenum, New York (1998)
11. Brown, M., Grundy, W., Lin, D., Christianini, N., Sugnet, C., Haussler, D.: Support Vector Machines Classication of Microarray Gene Expression Data, Technical Report UCSC-CRL 99-09, Department of Computer Science, University California Santa Cruz, Santa Cruz, CA (1999)
12. Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of discrimination methods for the classication of tumors using gene expression data. Technical Report 576, Department of Statistics, UC Berkeley (2000)
13. Yeoh, E.J., Ross, M.E., Shurtle, S.A., Williams, W.K., Patel, D., Mahfouz, R., Behm, F.G., Raimondi, S.C., Relling, M.V., Patel, A., Cheng, C., Campana, D., Wilkins, D., Zhou, X., Li, J., Liu, H., Pui, C.H., Evans, W.E., Naeve, C., Wong, L., Downing, J.R.: Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 1(2), 133-143 (2002)
14. 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 52(1-2), 91-118 (2003)