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Soft Computing
Published in: Soft Computing 2/2010

01-01-2010 | Focus

k-Top Scoring Pair Algorithm for feature selection in SVM with applications to microarray data classification

Authors: Sejong Yoon, Saejoon Kim

Published in: Soft Computing | Issue 2/2010

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Abstract

Top Scoring Pair (TSP) and its ensemble counterpart, k-Top Scoring Pair (k-TSP), were recently introduced as competitive options for solving classification problems of microarray data. However, support vector machine (SVM) which was compared with these approaches is not equipped with feature or variable selection mechanism while TSP itself is a kind of variable selection algorithm. Moreover, an ensemble of SVMs should also be considered as a possible competitor to k-TSP. In this work, we conducted a fair comparison between TSP and SVM-recursive feature elimination (SVM-RFE) as the feature selection method for SVM. We also compared k-TSP with two ensemble methods using SVM as their base classifier. Results on ten public domain microarray data indicated that TSP family classifiers serve as good feature selection schemes which may be combined effectively with other classification methods.
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Footnotes
Literature
Alizadeh AA, Eisen MB, Davis EE et al (2000) Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature 403(6769):503–511CrossRef
Alon U, Barkai N, Notterman DA et al (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA 96(12):6745–6750CrossRef
Beer DG, Kardia SL, Huang CC et al (2002) Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 8(8):816–824
Buciu I, Kotropoulos C, Pitas I (2006) Demonstrating the stability of support vector machines for classification. Signal Process 86(9):2364–2380CrossRef
Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167CrossRef
Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46CrossRef
Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3(2):185–205CrossRefMathSciNet
Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139MATHCrossRefMathSciNet
Geman D, d’Avignon C, Naiman D, Winslow R (2004) Classifying gene expression profiles from pairwise mrna comparisons. Stat Appl Genet Mol Biol 3(1):19MathSciNet
Golub TR, Slonim DK, Tamayo P et al (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531–537CrossRef
Gordon GJ, Jensen RV, li Hsiao L et al (2002) Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Res 62:4963–4967
Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1–3):389–422MATHCrossRef
Joachims T (1999) Making large-scale support vector machine learning practical. In: Schölkopf B, Burges CJC, Smola AJ (eds) Advances in kernel methods: support vector learning. MIT Press, Cambridge, pp 169–184
Kim HC, Pang S, Je HM, Kim D, Bang SY (2003) Constructing support vector machine ensemble. Pattern Recognit 36(12):2757–2767MATHCrossRef
Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238CrossRef
Platt JC (1999) Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B, Burges CJC, Smola AJ (eds) Advances in kernel methods: support vector learning. MIT Press, Cambridge, pp 185–208
Pomeroy SL, Tamayo P, Gaasenbeek M et al (2002) Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415(6870):436–442CrossRef
Rosenwald A, Wright G, Chan WC et al (2002) The use of molecular profiling to predict survival after chemotherapy for diffuse large-b-cell lymphoma. N Engl J Med 346(25):1937–1947CrossRef
Shipp MA, Ross KN, Tamayo P et al (2002) Diffuse large b-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 8(1):68–74CrossRef
Singh D, Febbo PG, Ross K et al (2002) Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1(2):203–209CrossRef
Tan AC, Naiman DQ, Xu L, Winslow RL, Geman D (2005) Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics 21(20):3896–3904CrossRef
Vapnik VN (1998) Statistical Learning Theory. Wiley-Interscience
Wigle DA, Jurisica I, Radulovich N et al (2002) Molecular profiling of non-small cell lung cancer and correlation with disease-free survival. Cancer Res 62:3005–3008
Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann
Metadata
Title
k-Top Scoring Pair Algorithm for feature selection in SVM with applications to microarray data classification
Authors
Sejong Yoon
Saejoon Kim
Publication date
01-01-2010
Publisher
Springer-Verlag
Published in
Soft Computing / Issue 2/2010
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-009-0437-x

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