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Volumn 1, Issue 1-2, 2012, Pages 47-61

Threshold-based feature selection techniques for high-dimensional bioinformatics data

Author keywords

Bioinformatics; Correlation matrix; Feature selection; Frobenius norm; Kendall's Tau rank correlation; Threshold based feature selection

Indexed keywords

ANALYTIC METHOD; ARTICLE; BIOINFORMATICS; CLASSIFICATION; CORRELATION ANALYSIS; EMPIRICAL RESEARCH; GENE EXPRESSION; GENETIC ALGORITHM; HUMAN; MICROARRAY ANALYSIS; OVARY CANCER; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SUPPORT VECTOR MACHINE; THRESHOLD BASED FEATURE SELECTION TECHNIQUE;

EID: 84897692889     PISSN: None     EISSN: 21926670     Source Type: Journal    
DOI: 10.1007/s13721-012-0006-6     Document Type: Article
Times cited : (55)

References (52)
  • 2
    • 0033536012 scopus 로고    scopus 로고
    • Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays
    • Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Nal Acad Sci USA 96(12): 6745-6750.
    • (1999) Proc Nal Acad Sci USA , vol.96 , Issue.12 , pp. 6745-6750
    • Alon, U.1    Barkai, N.2    Notterman, D.A.3    Gish, K.4    Ybarra, S.5    Mack, D.6    Levine, A.J.7
  • 3
    • 22944482840 scopus 로고    scopus 로고
    • Attribute clustering for grouping, selection, and classification of gene expression data
    • Au W-H, Chan KCC, Wong AKC, Wang Y (2005) Attribute clustering for grouping, selection, and classification of gene expression data. IEEE/ACM Trans Comput Biol Bioinform 2(2): 83-101.
    • (2005) IEEE/ACM Trans Comput Biol Bioinform , vol.2 , Issue.2 , pp. 83-101
    • Au, W.-H.1    Chan, K.C.C.2    Wong, A.K.C.3    Wang, Y.4
  • 4
    • 0028468293 scopus 로고
    • Using mutual information for selecting features in supervised neural net learning
    • Battiti R (1994) Using mutual information for selecting features in supervised neural net learning. IEEE Trans Neural Netw 5(4): 537-550.
    • (1994) IEEE Trans Neural Netw , vol.5 , Issue.4 , pp. 537-550
    • Battiti, R.1
  • 9
    • 0037342510 scopus 로고    scopus 로고
    • Comparisons and validation of statistical clustering techniques for microarray gene expression data
    • Datta S, Datta S (2003) Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19(4): 459-466.
    • (2003) Bioinformatics , vol.19 , Issue.4 , pp. 459-466
    • Datta, S.1    Datta, S.2
  • 10
  • 11
  • 12
    • 0003024008 scopus 로고
    • On the handling of continuous-valued attributes in decision tree generation
    • Fayyad UM, Irani KB (1992) On the handling of continuous-valued attributes in decision tree generation. Mach Learn 8: 87-102.
    • (1992) Mach Learn , vol.8 , pp. 87-102
    • Fayyad, U.M.1    Irani, K.B.2
  • 13
    • 2942731012 scopus 로고    scopus 로고
    • An extensive empirical study of feature selection metrics for text classification
    • Forman G (2003) An extensive empirical study of feature selection metrics for text classification. J Mach Learn Res 3: 1289-1305.
    • (2003) J Mach Learn Res , vol.3 , pp. 1289-1305
    • Forman, G.1
  • 14
    • 0033636139 scopus 로고    scopus 로고
    • Support vector machine classification and validation of cancer tissue samples using microarray expression data
    • Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16(10): 906-914.
    • (2000) Bioinformatics , vol.16 , Issue.10 , pp. 906-914
    • Furey, T.S.1    Cristianini, N.2    Duffy, N.3    Bednarski, D.W.4    Schummer, M.5    Haussler, D.6
  • 15
    • 0004236492 scopus 로고    scopus 로고
    • 3rd edn. The Johns Hopkins University Press, Baltimore
    • Golub GH, CF van Loan (1996) Matrix computations, 3rd edn. The Johns Hopkins University Press, Baltimore.
    • (1996) Matrix computations
    • Golub, G.H.1    van Loan, C.F.2
  • 17
    • 48349117298 scopus 로고    scopus 로고
    • A novel and efficient technique for identification and classification of gpcrs
    • Gupta R, Mittal A, Singh K (2008) A novel and efficient technique for identification and classification of gpcrs. IEEE Trans Inf Technol Biomed 12(4): 541-548.
    • (2008) IEEE Trans Inf Technol Biomed , vol.12 , Issue.4 , pp. 541-548
    • Gupta, R.1    Mittal, A.2    Singh, K.3
  • 18
    • 33745561205 scopus 로고    scopus 로고
    • An introduction to variable and feature selection
    • Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3: 1157-1182.
    • (2003) J Mach Learn Res , vol.3 , pp. 1157-1182
    • Guyon, I.1    Elisseeff, A.2
  • 19
    • 0242410408 scopus 로고    scopus 로고
    • Benchmarking attribute selection techniques for discrete class data mining
    • Hall MA, Holmes G (2003) Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans Knowl Data Eng 15(6): 392-398.
    • (2003) IEEE Trans Knowl Data Eng , vol.15 , Issue.6 , pp. 392-398
    • Hall, M.A.1    Holmes, G.2
  • 22
    • 3042532685 scopus 로고    scopus 로고
    • Filter versus wrapper gene selection approaches in dna microarray domains
    • (Data Mining in Genomics and Proteomics)
    • Inza I, Larraaga P, Blanco R, Cerrolaza AJ (2004) Filter versus wrapper gene selection approaches in dna microarray domains. Artif Intell Med 31(2): 91-103 (Data Mining in Genomics and Proteomics).
    • (2004) Artif Intell Med , vol.31 , Issue.2 , pp. 91-103
    • Inza, I.1    Larraaga, P.2    Blanco, R.3    Cerrolaza, A.J.4
  • 23
    • 25444528447 scopus 로고    scopus 로고
    • Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes
    • Jirapech-Umpai T, Aitken S (2005) Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes. BMC Bioinform 6(1): 148.
    • (2005) BMC Bioinform , vol.6 , Issue.1 , pp. 148
    • Jirapech-Umpai, T.1    Aitken, S.2
  • 24
    • 34248647608 scopus 로고    scopus 로고
    • Stability of feature selection algorithms: a study on high-dimensional spaces
    • Kalousis A, Prados J, Hilario M (2007) Stability of feature selection algorithms: a study on high-dimensional spaces. Knowl Inf Syst 12(1): 95-116.
    • (2007) Knowl Inf Syst , vol.12 , Issue.1 , pp. 95-116
    • Kalousis, A.1    Prados, J.2    Hilario, M.3
  • 26
    • 0036891333 scopus 로고    scopus 로고
    • Using regression trees to classify fault-prone software modules
    • Khoshgoftaar TM, Allen EB, Deng J (2002) Using regression trees to classify fault-prone software modules. IEEE Trans Reliab 51(4): 455-462.
    • (2002) IEEE Trans Reliab , vol.51 , Issue.4 , pp. 455-462
    • Khoshgoftaar, T.M.1    Allen, E.B.2    Deng, J.3
  • 28
    • 84992726552 scopus 로고
    • Estimating attributes: analysis and extensions of RELIEF
    • Springer, New York
    • Kononenko I (1994) Estimating attributes: analysis and extensions of RELIEF. In: European conference on machine learning, Springer, New York, pp 171-182.
    • (1994) European conference on machine learning , pp. 171-182
    • Kononenko, I.1
  • 33
    • 0036139278 scopus 로고    scopus 로고
    • Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method
    • Li L, Weinberg CR, Darden TA, Pedersen LG (2001) Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17(12): 1131-1142.
    • (2001) Bioinformatics , vol.17 , Issue.12 , pp. 1131-1142
    • Li, L.1    Weinberg, C.R.2    Darden, T.A.3    Pedersen, L.G.4
  • 34
    • 0035224384 scopus 로고    scopus 로고
    • Feature selection for dna methylation based cancer classification
    • Model F (2001) Feature selection for dna methylation based cancer classification. Bioinformatics 17: 157-164(8).
    • (2001) Bioinformatics , vol.17 , pp. 157-164
    • Model, F.1
  • 35
    • 0037620663 scopus 로고    scopus 로고
    • Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference
    • Peddada SD, Lobenhofer EK, Li L, Afshari CA, Weinberg CR, Umbach DM (2003) Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference. Bioinformatics 19(7): 834-841.
    • (2003) Bioinformatics , vol.19 , Issue.7 , pp. 834-841
    • Peddada, S.D.1    Lobenhofer, E.K.2    Li, L.3    Afshari, C.A.4    Weinberg, C.R.5    Umbach, D.M.6
  • 37
    • 24344458137 scopus 로고    scopus 로고
    • Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy
    • 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): 1226-1238.
    • (2005) IEEE Trans Pattern Anal Mach Intell , vol.27 , Issue.8 , pp. 1226-1238
    • Peng, H.1    Long, F.2    Ding, C.3
  • 38
    • 23944451661 scopus 로고    scopus 로고
    • Microarray data mining: facing the challenges
    • Piatetsky-Shapiro G, Tamayo P (2003) Microarray data mining: facing the challenges. SIGKDD Explor Newsl 5(2): 1-5.
    • (2003) SIGKDD Explor Newsl , vol.5 , Issue.2 , pp. 1-5
    • Piatetsky-Shapiro, G.1    Tamayo, P.2
  • 40
    • 4744344959 scopus 로고    scopus 로고
    • Classification and knowledge discovery in protein databases
    • Biomedical Machine Learning
    • Radivojac P, Chawla NV, Dunker AK, Obradovic Z (2004) Classification and knowledge discovery in protein databases. J Biomed Inform 37(4): 224-239 Biomedical Machine Learning.
    • (2004) J Biomed Inform , vol.37 , Issue.4 , pp. 224-239
    • Radivojac, P.1    Chawla, N.V.2    Dunker, A.K.3    Obradovic, Z.4
  • 46
    • 17444386734 scopus 로고    scopus 로고
    • HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data
    • Wang Y, Makedon FS, Ford JC, Pearlman J (2005) HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data. Bioinformatics 21(8): 1530-1537.
    • (2005) Bioinformatics , vol.21 , Issue.8 , pp. 1530-1537
    • Wang, Y.1    Makedon, F.S.2    Ford, J.C.3    Pearlman, J.4
  • 47
  • 48
    • 1442275185 scopus 로고    scopus 로고
    • Learning when training data are costly: the effect of class distribution on tree induction
    • Weiss GM, Provost F (2003) Learning when training data are costly: the effect of class distribution on tree induction. J Artif Intell Res (19): 315-354.
    • (2003) J Artif Intell Res , vol.19 , pp. 315-354
    • Weiss, G.M.1    Provost, F.2
  • 52
    • 0035810997 scopus 로고    scopus 로고
    • Recursive partitioning for tumor classification with gene expression microarray data
    • Zhang H, Yu C-Y, Singer B, Xiong M (2001) Recursive partitioning for tumor classification with gene expression microarray data. Proc Natl Acad Sci USA 98(12): 6730-6735.
    • (2001) Proc Natl Acad Sci USA , vol.98 , Issue.12 , pp. 6730-6735
    • Zhang, H.1    Yu, C.-Y.2    Singer, B.3    Xiong, M.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.