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Volumn 36, Issue 10, 2006, Pages 1104-1125

Machine learning in bioinformatics: A brief survey and recommendations for practitioners

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; MACHINE LEARNING TECHNIQUES; MODEL PARAMETER SELECTION;

EID: 33746747476     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2005.09.002     Document Type: Article
Times cited : (87)

References (82)
  • 2
    • 84984932472 scopus 로고    scopus 로고
    • Exploring the new world of the genome with DNA microarrays
    • Brown P.O., and Botstein D. Exploring the new world of the genome with DNA microarrays. Nature Genetics 21 suppl. 33 (1999)
    • (1999) Nature Genetics , vol.21 , Issue.SUPPL. 33
    • Brown, P.O.1    Botstein, D.2
  • 3
    • 0035375137 scopus 로고    scopus 로고
    • Computational analysis of microarray data
    • Quackenbush J. Computational analysis of microarray data. Nature Rev. Genetics 2 (2001) 418
    • (2001) Nature Rev. Genetics , vol.2 , pp. 418
    • Quackenbush, J.1
  • 4
    • 0036898577 scopus 로고    scopus 로고
    • Microarray data normalization and transformation
    • Quackenbush J. Microarray data normalization and transformation. Nature Genetics 32 (2002) 496
    • (2002) Nature Genetics , vol.32 , pp. 496
    • Quackenbush, J.1
  • 5
    • 0033569406 scopus 로고    scopus 로고
    • Molecular classification of cancer: class discovery and class prediction by gene expression monitoring
    • Golub T.R., et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531
    • (1999) Science , vol.286 , pp. 531
    • Golub, T.R.1
  • 6
    • 0034598746 scopus 로고    scopus 로고
    • Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
    • Alizadeh A.A., et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403 (2000) 503
    • (2000) Nature , vol.403 , pp. 503
    • Alizadeh, A.A.1
  • 7
    • 0034954414 scopus 로고    scopus 로고
    • Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks
    • Khan J., et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Med. 7 (2001) 673
    • (2001) Nature Med. , vol.7 , pp. 673
    • Khan, J.1
  • 8
    • 0034602774 scopus 로고    scopus 로고
    • Knowledge-based analysis of microarray gene expression data by using support vector machines
    • Brown M.P.S., et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Nat. Acad. Sci. USA 97 (2000) 262
    • (2000) Proc. Nat. Acad. Sci. USA , vol.97 , pp. 262
    • Brown, M.P.S.1
  • 9
    • 0033636139 scopus 로고    scopus 로고
    • Support vector machine classification and validation of cancer tissue samples using microarray expression data
    • Furey T.S., et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16 (2000) 906
    • (2000) Bioinformatics , vol.16 , pp. 906
    • Furey, T.S.1
  • 10
    • 0038729565 scopus 로고    scopus 로고
    • Classification of multiple cancer types by multicategory support vector machines using gene expression data
    • Lee Y., and Lee C.K. Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics 19 (2003) 1132
    • (2003) Bioinformatics , vol.19 , pp. 1132
    • Lee, Y.1    Lee, C.K.2
  • 11
    • 0036139314 scopus 로고    scopus 로고
    • A neural networks classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells
    • Boland M.V., and Murphy R.F. A neural networks classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 17 (2001) 1213
    • (2001) Bioinformatics , vol.17 , pp. 1213
    • Boland, M.V.1    Murphy, R.F.2
  • 12
    • 0036303121 scopus 로고    scopus 로고
    • NETASA: neural network based prediction of solvent accessibility
    • Ahmad S., and Gromiha M.M. NETASA: neural network based prediction of solvent accessibility. Bioinformatics 18 (2002) 819
    • (2002) Bioinformatics , vol.18 , pp. 819
    • Ahmad, S.1    Gromiha, M.M.2
  • 13
    • 0036677954 scopus 로고    scopus 로고
    • Neural network predicts the sequence of TP53 gene based on DNA chip
    • Spicker J.S., et al. Neural network predicts the sequence of TP53 gene based on DNA chip. Bioinformatics 18 (2002) 1133
    • (2002) Bioinformatics , vol.18 , pp. 1133
    • Spicker, J.S.1
  • 14
    • 0742289562 scopus 로고    scopus 로고
    • Pediatric acute lymphoblastic leukemia (ALL) gene expression signatures classify an independent cohort of adult ALL patients
    • Kohlmann A., et al. Pediatric acute lymphoblastic leukemia (ALL) gene expression signatures classify an independent cohort of adult ALL patients. Leukemia 18 (2004) 63
    • (2004) Leukemia , vol.18 , pp. 63
    • Kohlmann, A.1
  • 15
    • 0346252360 scopus 로고    scopus 로고
    • Classification of protein quaternary structure with support vector machine
    • Zhang S.W., et al. Classification of protein quaternary structure with support vector machine. Bioinformatics 19 (2003) 2390
    • (2003) Bioinformatics , vol.19 , pp. 2390
    • Zhang, S.W.1
  • 16
    • 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 C.R., Darden T.A., and Pedersen L.G. Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17 (2001) 1131
    • (2001) Bioinformatics , vol.17 , pp. 1131
    • Li, L.1    Weinberg, C.R.2    Darden, T.A.3    Pedersen, L.G.4
  • 17
    • 18544375333 scopus 로고    scopus 로고
    • MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia
    • Armstrong S.A., et al. MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nature Genetics 30 (2002) 41
    • (2002) Nature Genetics , vol.30 , pp. 41
    • Armstrong, S.A.1
  • 18
    • 0345832339 scopus 로고    scopus 로고
    • Protein β turn prediction using nearest neighbour method
    • Kim S. Protein β turn prediction using nearest neighbour method. Bioinformatics 20 (2004) 40
    • (2004) Bioinformatics , vol.20 , pp. 40
    • Kim, S.1
  • 19
    • 0034680102 scopus 로고    scopus 로고
    • Molecular portraits of human breast tumours
    • Perou C.M., et al. Molecular portraits of human breast tumours. Nature 406 (2000) 747
    • (2000) Nature , vol.406 , pp. 747
    • Perou, C.M.1
  • 20
    • 18244409687 scopus 로고    scopus 로고
    • Gene expression profiling predicts clinical outcome of breast cancer
    • van't Veer L.J., et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415 (2002) 484
    • (2002) Nature , vol.415 , pp. 484
    • van't Veer, L.J.1
  • 21
    • 0035939903 scopus 로고    scopus 로고
    • Delineation of prognostic biomarkers in prostate cancer
    • Dhanasekaran S.M., et al. Delineation of prognostic biomarkers in prostate cancer. Nature 412 (2001) 822
    • (2001) Nature , vol.412 , pp. 822
    • Dhanasekaran, S.M.1
  • 22
    • 0038109937 scopus 로고    scopus 로고
    • Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data
    • Getz G., et al. Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data. Bioinformatics 19 (2003) 1079
    • (2003) Bioinformatics , vol.19 , pp. 1079
    • Getz, G.1
  • 23
    • 0036166753 scopus 로고    scopus 로고
    • Linear modes of gene expression determined by independent component analysis
    • Liebermeister W. Linear modes of gene expression determined by independent component analysis. Bioinformatics 18 (2002) 51
    • (2002) Bioinformatics , vol.18 , pp. 51
    • Liebermeister, W.1
  • 24
    • 0034909184 scopus 로고    scopus 로고
    • Visualization of expression clusters using Sammon's non-linear mapping
    • Ewing R.M., and Cherry J.M. Visualization of expression clusters using Sammon's non-linear mapping. Bioinformatics 17 (2001) 658
    • (2001) Bioinformatics , vol.17 , pp. 658
    • Ewing, R.M.1    Cherry, J.M.2
  • 25
    • 0033027794 scopus 로고    scopus 로고
    • Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation
    • Tamayo P., et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Nat. Acad. Sci. USA 96 (1999) 2907
    • (1999) Proc. Nat. Acad. Sci. USA , vol.96 , pp. 2907
    • Tamayo, P.1
  • 26
    • 0037389951 scopus 로고    scopus 로고
    • PCA learning for sparse high-dimensional data
    • Hoyle D.C., and Rattray M. PCA learning for sparse high-dimensional data. Europhys. Lett. 62 (2003) 117
    • (2003) Europhys. Lett. , vol.62 , pp. 117
    • Hoyle, D.C.1    Rattray, M.2
  • 27
    • 0141506120 scopus 로고    scopus 로고
    • Characterising proteolytic cleavage site activity using bio-basis function neural network
    • Thomson R., Hodgman C., Yang Z.R., and Doyle A.K. Characterising proteolytic cleavage site activity using bio-basis function neural network. Bioinformatics 19 (2003) 1741
    • (2003) Bioinformatics , vol.19 , pp. 1741
    • Thomson, R.1    Hodgman, C.2    Yang, Z.R.3    Doyle, A.K.4
  • 28
    • 0001020401 scopus 로고
    • Recent advances in error rate estimation
    • Hand D.J. Recent advances in error rate estimation. Pattern Recogn. Lett. 5 (1986) 335
    • (1986) Pattern Recogn. Lett. , vol.5 , pp. 335
    • Hand, D.J.1
  • 29
    • 63249112814 scopus 로고
    • Dimensionality and sample size considerations in pattern recognition practice
    • Krishnaiah P.R., and Kanal L.N. (Eds), North Holland, Amsterdam
    • Jain A.K., and Chandrasekharan B. Dimensionality and sample size considerations in pattern recognition practice. In: Krishnaiah P.R., and Kanal L.N. (Eds). Handbook of Statistics vol. 2 (1982), North Holland, Amsterdam 835
    • (1982) Handbook of Statistics , vol.2 , pp. 835
    • Jain, A.K.1    Chandrasekharan, B.2
  • 30
    • 0006586801 scopus 로고
    • Large sample approximations and asymptotic expansions of classification statistics
    • Krishnaiah P.R., and Kanal L.N. (Eds), North Holland, Amsterdam
    • Siotani M. Large sample approximations and asymptotic expansions of classification statistics. In: Krishnaiah P.R., and Kanal L.N. (Eds). Handbook of Statistics vol. 2 (1982), North Holland, Amsterdam 61
    • (1982) Handbook of Statistics , vol.2 , pp. 61
    • Siotani, M.1
  • 31
    • 0026120032 scopus 로고
    • Small sample size effects in statistical pattern recognition: recommendations for practitioners
    • Raudys S., and Jain A.K. Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans. Pattern Anal. Machine Intell. 13 (1991) 252
    • (1991) IEEE Trans. Pattern Anal. Machine Intell. , vol.13 , pp. 252
    • Raudys, S.1    Jain, A.K.2
  • 32
    • 0036489046 scopus 로고    scopus 로고
    • Comparison of discrimination methods for the classification of tumors using gene expression data
    • Dudoit S., Fridlyand J., and Speed T.P. Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Stat. Ass. 97 (2002) 77
    • (2002) J. Am. Stat. Ass. , vol.97 , pp. 77
    • Dudoit, S.1    Fridlyand, J.2    Speed, T.P.3
  • 33
    • 10244252786 scopus 로고    scopus 로고
    • Systematic benchmarking of microarray data classification: assessing the role of nonlinearity and dimensionality reduction
    • Pochet N., De Smet F., Suykens J.A., and De Moor B.L. Systematic benchmarking of microarray data classification: assessing the role of nonlinearity and dimensionality reduction. Bioinformatics 20 (2004) 3185
    • (2004) Bioinformatics , vol.20 , pp. 3185
    • Pochet, N.1    De Smet, F.2    Suykens, J.A.3    De Moor, B.L.4
  • 34
    • 0036738906 scopus 로고    scopus 로고
    • Determination of minimum sample size and discriminatory expression patterns in microarray data
    • Hwang D., et al. Determination of minimum sample size and discriminatory expression patterns in microarray data. Bioinformatics 18 (2002) 1184
    • (2002) Bioinformatics , vol.18 , pp. 1184
    • Hwang, D.1
  • 35
    • 0038237368 scopus 로고    scopus 로고
    • Estimating dataset size requirements for classifying DNA microarray data
    • Mukherjee S., et al. Estimating dataset size requirements for classifying DNA microarray data. J. Comput. Biol. 10 (2003) 119
    • (2003) J. Comput. Biol. , vol.10 , pp. 119
    • Mukherjee, S.1
  • 37
    • 33746728746 scopus 로고    scopus 로고
    • H.C. Kraemer, S. Thiemann, How many subjects, Statistical Power Analysis in Research, Sage, CA, 1987.
  • 39
    • 0346789285 scopus 로고    scopus 로고
    • Sample size determination: a review
    • Adcock C. Sample size determination: a review. Stat. 46 (1997) 262
    • (1997) Stat. , vol.46 , pp. 262
    • Adcock, C.1
  • 40
    • 0031676721 scopus 로고    scopus 로고
    • What size test set gives you good error estimates
    • Guyon I., et al. What size test set gives you good error estimates. IEEE Trans. Pattern Anal. Machine Intell. 20 (1998) 52
    • (1998) IEEE Trans. Pattern Anal. Machine Intell. , vol.20 , pp. 52
    • Guyon, I.1
  • 42
    • 0000959484 scopus 로고    scopus 로고
    • CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts
    • Xing E.P., and Karp R.M. CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts. Bioinformatics 17 Suppl. 1 (2001) 306
    • (2001) Bioinformatics , vol.17 , Issue.SUPPL. 1 , pp. 306
    • Xing, E.P.1    Karp, R.M.2
  • 43
    • 33746754629 scopus 로고    scopus 로고
    • E.P. Xing, M.I. Jordan, R.M. Karp, Feature selection for high-dimensional genomic microarray data, in: Proceedings of the 18th International Conference on Machine Learning, 2001.
  • 44
    • 0035184851 scopus 로고    scopus 로고
    • Biomarker identification by feature wrappers
    • Xiong M., Fang X., and Zhao J. Biomarker identification by feature wrappers. Genome Res. 11 (2001) 1878
    • (2001) Genome Res. , vol.11 , pp. 1878
    • Xiong, M.1    Fang, X.2    Zhao, J.3
  • 45
    • 0033536012 scopus 로고    scopus 로고
    • Broad patterns of gene expression revealed by clustering analysis of tumour and normal colon tissues probed by oligonucleotide arrays
    • Alon U., et al. Broad patterns of gene expression revealed by clustering analysis of tumour and normal colon tissues probed by oligonucleotide arrays. Proc. Nat. Acad. Sci. USA 96 (1999) 6745
    • (1999) Proc. Nat. Acad. Sci. USA , vol.96 , pp. 6745
    • Alon, U.1
  • 46
    • 0031078007 scopus 로고    scopus 로고
    • Feature-selection: evaluation, application, and small sample performance
    • Jain A.K., and Zongker D. Feature-selection: evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Machine Intell. 19 (1997) 152
    • (1997) IEEE Trans. Pattern Anal. Machine Intell. , vol.19 , pp. 152
    • Jain, A.K.1    Zongker, D.2
  • 47
    • 0347379706 scopus 로고    scopus 로고
    • Multi-resolution estimates of classification complexity
    • Singh S. Multi-resolution estimates of classification complexity. IEEE Trans. Pattern Anal. Machine Intell. 25 (2003) 1534
    • (2003) IEEE Trans. Pattern Anal. Machine Intell. , vol.25 , pp. 1534
    • Singh, S.1
  • 48
    • 0042074099 scopus 로고    scopus 로고
    • PRISM-a novel framework for pattern recognition
    • Singh S. PRISM-a novel framework for pattern recognition. Pattern Anal. Appl. 6 (2003) 131
    • (2003) Pattern Anal. Appl. , vol.6 , pp. 131
    • Singh, S.1
  • 49
    • 33746712793 scopus 로고    scopus 로고
    • J. Mao, K. Mohiuddin, A.K. Jain, Parsimonious network design and feature selection through node pruning, in: Proceedings of 12th ICPR, 1994, pp. 622.
  • 50
    • 0027610652 scopus 로고
    • A more efficient branch and bound algorithm for feature selection
    • Yu B., and Yuan B. A more efficient branch and bound algorithm for feature selection. Pattern Recogn. 26 (1993) 883
    • (1993) Pattern Recogn. , vol.26 , pp. 883
    • Yu, B.1    Yuan, B.2
  • 51
    • 84910828260 scopus 로고
    • Application of the Karhunen-Loève expansion to feature selection and ordering
    • Fukunaga K., and Koontz W.L.G. Application of the Karhunen-Loève expansion to feature selection and ordering. IEEE Trans. Comput. 19 (1970) 311
    • (1970) IEEE Trans. Comput. , vol.19 , pp. 311
    • Fukunaga, K.1    Koontz, W.L.G.2
  • 52
    • 0347243182 scopus 로고    scopus 로고
    • Nonlinear component analysis as a kernel eigenvalue problem
    • Schölkopf B., Smola A., and Müller K. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10 (1998) 1299
    • (1998) Neural Comput. , vol.10 , pp. 1299
    • Schölkopf, B.1    Smola, A.2    Müller, K.3
  • 53
    • 4444223563 scopus 로고    scopus 로고
    • Robust PCA and classification in biosciences
    • Hubert M., and Engelen S. Robust PCA and classification in biosciences. Bioinformatics 20 (2004) 1728
    • (2004) Bioinformatics , vol.20 , pp. 1728
    • Hubert, M.1    Engelen, S.2
  • 54
    • 0032091512 scopus 로고    scopus 로고
    • Non-linear dimensionality reduction techniques for unsupervised feature extraction
    • De Backer S., Naud A., and Scheunders P. Non-linear dimensionality reduction techniques for unsupervised feature extraction. Pattern Recogn. Lett. 20 (1998) 711
    • (1998) Pattern Recogn. Lett. , vol.20 , pp. 711
    • De Backer, S.1    Naud, A.2    Scheunders, P.3
  • 55
    • 84887006810 scopus 로고
    • A nonlinear mapping for data structure analysis
    • Sammon J.W. A nonlinear mapping for data structure analysis. IEEE Trans. Comput. 18 (1969) 401
    • (1969) IEEE Trans. Comput. , vol.18 , pp. 401
    • Sammon, J.W.1
  • 56
    • 3342991601 scopus 로고    scopus 로고
    • Gene selection and classification from microarray data using kernel machine
    • Cho J.H., Lee D., Park J.H., and Lee I.B. Gene selection and classification from microarray data using kernel machine. FEBS Lett. 571 (2004) 93
    • (2004) FEBS Lett. , vol.571 , pp. 93
    • Cho, J.H.1    Lee, D.2    Park, J.H.3    Lee, I.B.4
  • 57
    • 0015126219 scopus 로고
    • Redundancy in feature extraction
    • Heydorn R.P. Redundancy in feature extraction. IEEE Trans. Comput. 20 (1971) 1051
    • (1971) IEEE Trans. Comput. , vol.20 , pp. 1051
    • Heydorn, R.P.1
  • 58
    • 47749125980 scopus 로고    scopus 로고
    • A robustification of the Jarque Bera test of normality
    • Antoch J. (Ed), Springer, New York
    • Brys G., Hubert M., and Struyf A. A robustification of the Jarque Bera test of normality. In: Antoch J. (Ed). COMPSTAT 2004 Symposium (2004), Springer, New York
    • (2004) COMPSTAT 2004 Symposium
    • Brys, G.1    Hubert, M.2    Struyf, A.3
  • 61
    • 0034960264 scopus 로고    scopus 로고
    • Missing value estimation methods for DNA microarrays
    • Troyanskaya O., et al. Missing value estimation methods for DNA microarrays. Bioinformatics 17 (2001) 520
    • (2001) Bioinformatics , vol.17 , pp. 520
    • Troyanskaya, O.1
  • 62
    • 0242643743 scopus 로고    scopus 로고
    • A Bayesian missing value estimation method for gene expression profile data
    • Oba S., Sato M., Takemasa I., Monden M., Matsubara K., and Ishii S. A Bayesian missing value estimation method for gene expression profile data. Bioinformatics 19 (2003) 2088
    • (2003) Bioinformatics , vol.19 , pp. 2088
    • Oba, S.1    Sato, M.2    Takemasa, I.3    Monden, M.4    Matsubara, K.5    Ishii, S.6
  • 63
    • 18344396568 scopus 로고    scopus 로고
    • Minimum information about a microarray experiment (MIAME)-towards standards for microarray data
    • Brazma A., et al. Minimum information about a microarray experiment (MIAME)-towards standards for microarray data. Nature Genetics 29 (2001) 365
    • (2001) Nature Genetics , vol.29 , pp. 365
    • Brazma, A.1
  • 64
    • 0035971103 scopus 로고    scopus 로고
    • Affymetrix settles suit, fixes mouse chip
    • Marshall E. Affymetrix settles suit, fixes mouse chip. Science 291 (2001) 2535
    • (2001) Science , vol.291 , pp. 2535
    • Marshall, E.1
  • 65
    • 0032441150 scopus 로고    scopus 로고
    • Cluster analysis and display of genome-wide expression patterns
    • Eisen M.B., et al. Cluster analysis and display of genome-wide expression patterns. Proc. Nat. Acad. Sci. USA 95 (1998) 14863
    • (1998) Proc. Nat. Acad. Sci. USA , vol.95 , pp. 14863
    • Eisen, M.B.1
  • 66
    • 0033028596 scopus 로고    scopus 로고
    • Systematic determination of genetic network architecture
    • Tavazoie S., et al. Systematic determination of genetic network architecture. Nature Genetics 22 (1999) 281
    • (1999) Nature Genetics , vol.22 , pp. 281
    • Tavazoie, S.1
  • 68
    • 0038391443 scopus 로고    scopus 로고
    • Bagging to improve the accuracy of a clustering procedure
    • Dudoit S., and Fridlyand J. Bagging to improve the accuracy of a clustering procedure. Bioinformatics 19 (2003) 1090
    • (2003) Bioinformatics , vol.19 , pp. 1090
    • Dudoit, S.1    Fridlyand, J.2
  • 70
    • 37049239596 scopus 로고
    • A computer program for classifying plants
    • Rogers D.J., and Tanimoto T.T. A computer program for classifying plants. Science 132 (1960) 1115
    • (1960) Science , vol.132 , pp. 1115
    • Rogers, D.J.1    Tanimoto, T.T.2
  • 72
    • 33747044600 scopus 로고
    • Metric and Euclidean properties of dissimilarity coefficients
    • Gower J.C., and Legendre P. Metric and Euclidean properties of dissimilarity coefficients. J. Classif. 5 (1986) 5
    • (1986) J. Classif. , vol.5 , pp. 5
    • Gower, J.C.1    Legendre, P.2
  • 73
    • 0000732090 scopus 로고
    • Evaluation of protein molecules
    • Munro H.N. (Ed), Academic Press, New York
    • Jukes T.H., and Cantor C.R. Evaluation of protein molecules. In: Munro H.N. (Ed). Mammalian Protein Metabolism III (1969), Academic Press, New York 21
    • (1969) Mammalian Protein Metabolism III , pp. 21
    • Jukes, T.H.1    Cantor, C.R.2
  • 74
    • 0001330098 scopus 로고
    • A general coefficient of similarity and some of its properties
    • Gower J.C. A general coefficient of similarity and some of its properties. Biometrics 27 (1971) 857
    • (1971) Biometrics , vol.27 , pp. 857
    • Gower, J.C.1
  • 75
    • 0001276815 scopus 로고
    • Distance between populations on the basis of attribute data
    • Balakishnan V., and Sanghvi L.D. Distance between populations on the basis of attribute data. Biometrics 24 (1968) 859
    • (1968) Biometrics , vol.24 , pp. 859
    • Balakishnan, V.1    Sanghvi, L.D.2
  • 76
    • 0000235019 scopus 로고
    • A study of standardisation of variables in cluster analysis
    • Milligan G.W., and Cooper M.C. A study of standardisation of variables in cluster analysis. J. Classif. 5 (1988) 181
    • (1988) J. Classif. , vol.5 , pp. 181
    • Milligan, G.W.1    Cooper, M.C.2
  • 78
    • 0033220832 scopus 로고    scopus 로고
    • Meta analysis of classification algorithms for pattern recognition
    • Sohn S.Y. Meta analysis of classification algorithms for pattern recognition. IEEE Trans. Pattern Anal. Machine Intell. 21 (1999) 1137
    • (1999) IEEE Trans. Pattern Anal. Machine Intell. , vol.21 , pp. 1137
    • Sohn, S.Y.1
  • 80
    • 0016552259 scopus 로고
    • Mathematical methods of feature selection in pattern recognition
    • Kittler J. Mathematical methods of feature selection in pattern recognition. Int. J. Man-Machine Stud. (1975) 609
    • (1975) Int. J. Man-Machine Stud. , pp. 609
    • Kittler, J.1
  • 81
    • 12144288329 scopus 로고    scopus 로고
    • Is combining classifiers with stacking better than selecting the best one
    • Dzeroski S., and Zenko B. Is combining classifiers with stacking better than selecting the best one. Machine Learn. 54 (2004) 255
    • (2004) Machine Learn. , vol.54 , pp. 255
    • Dzeroski, S.1    Zenko, B.2
  • 82
    • 0013175523 scopus 로고    scopus 로고
    • G. Giacinto, F. Roli, A theoretical framework for dynamic classifier selection, in: Proceedings of the 14th ICPR, 2000, pp. 2008.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.