Skip to main content

Advertisement

SpringerLink
Log in

Meta-classifiers for high-dimensional, small sample classification for gene expression analysis

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Classification using small sample size (limited number of samples) with high dimension is a challenging problem in both machine learning and medicine as there are a wide variety of possible modeling approaches. Furthermore, it is not always clear which method is optimal for a prediction task. Different modeling choices include feature selection (dimensionality reduction), classification algorithms, and ensemble selection. There are several possible combinations of these methods, and it is not always clear which is the best. In the previous works, researchers show that evolutionary computation is useful to build an ensemble from the pairs of feature selection and classification algorithms. However, there are several parameters to be determined for the evolutionary computation and it requires computational time for the optimization. In this paper, we attempt to improve the approach by adopting meta-classification with the farthest-first clustering algorithm. The effectiveness and accuracy of our method are validated by experiments on four real microarray datasets (colon, breast, prostate and lymphoma cancers) publicly available. The results confirm that the proposed method outperforms single individual classifiers and other alternatives (standard genetic algorithm, and methods from literature).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Abbreviations

AVG:

Average

CC:

Cosine coefficient

CF:

Classification

DCGA:

Deterministic crowding genetic algorithm

DLDA:

Diagonal linear discriminant analysis

ED:

Euclidean distance

F1–F4:

Fitness functions

FS:

Feature selection

G:

The number of genes

G1–G2:

Global ranking feature selection methods

GA:

Genetic algorithm

IG:

Information gain

IV:

Ideal vector

KNN:

K-nearest neighbor

KNNC:

KNN with cosine coefficient

KNNE:

KNN with Euclidean distance

KNNP:

KNN with Pearson correlation

KNNS:

KNN with Spearman correlation

LOOCV:

Leave-one-out cross-validation

M :

The number of classification algorithms

MDL:

Minimum description length

MI:

Mutual information

MLP:

Multi-layer perceptron

N :

The number of feature selection methods

NNGE:

Non-nested generalized exemplars

P :

The number of training samples

PAM:

Prediction analysis with microarray

PC:

Pearson correlation

PCP:

Pattern classification program

SNR:

Signal-to-noise ratio

SP:

Spearman correlation

SPEGASOS:

Stochastic variant of primal estimated sub-gradient solver for SVM

SVM:

Support vector machine

SVML:

Linear SVM

TS:

Training sample

References

  1. Psomopoulos FE, Mitkas PA (2010) Bioinformatics algorithm development for grid environments. J Syst Softw 83:1249–1257

    Article  Google Scholar 

  2. Slonim DK (2002) From patterns to pathways: gene expression data analysis comes of age. Nat Genet 32:502–508

    Article  Google Scholar 

  3. Braga-Neto U (2007) Fads and fallacies in the name of small-sample microarray classification. IEEE Signal Process Mag 24:91–99

    Article  Google Scholar 

  4. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Boston

    MATH  Google Scholar 

  5. Kim KJ, Cho SB (2008) An evolutionary algorithm approach to optimal ensemble classifiers for DNA microarray data analysis. IEEE Trans Evol Comput 12:377–388

    Article  Google Scholar 

  6. Xie X, Ho JWK, Murhpy C, Kaiser G, Xu B, Chen TY (2011) Testing and validating machine learning classifiers by metamorphic testing. J Syst Softw 84:544–558

    Article  Google Scholar 

  7. Saeys Y, Inza I, Larranaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23:2507–2517

    Article  Google Scholar 

  8. Blanco R, Larranaga P, Inza I, Sierra B (2004) Gene selection for cancer classification using wrapper approaches. Int J Pattern Recognit Artif Intell 18:1373–1390

    Article  Google Scholar 

  9. Inza I, Larranaga P, Blanco R, Cerrolaza AJ (2004) Filter versus wrapper gene selection approaches in DNA microarray domains. Artif Intell Med 31:91–103

    Article  Google Scholar 

  10. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422

    Article  MATH  Google Scholar 

  11. Su Y, Murali TM, Pavlovic V, Schaffer M, Kasif S (2003) RankGene: identification of diagnostic genes based on expression data. Bioinformatics 19:1578–1579

    Article  Google Scholar 

  12. Liu H, Liu L, Zhang H (2010) Ensemble gene selection by grouping for microarray data classification. J Biomed Inform 43:81–87

    Article  Google Scholar 

  13. Buturovic LJ (2006) PCP: a program for supervised classification of gene expression profiles. Bioinformatics 22:245–247

    Article  Google Scholar 

  14. Diaz-Uriarte R, de Andres SA (2006) Gene selection and classification of microarray data using random forest. BMC Bioinform 7:3

    Article  Google Scholar 

  15. Dettling M (2004) Bagboosting for tumor classification with gene expression data. Bioinformatics 20:3583–3593

    Article  Google Scholar 

  16. Jirapech-Umpai T, Aitken S (2005) Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes. BMC Bioinform 6:148

    Article  Google Scholar 

  17. 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 17:1131–1142

    Article  Google Scholar 

  18. Dudoit S, Fridlyand J, Speed TP (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 97:77–87

    Article  MathSciNet  MATH  Google Scholar 

  19. Cho SB, Won HH (2003) Data mining for gene expression profiles from DNA microarray. Int J Softw Eng Knowl Eng 13:593–608

    Article  Google Scholar 

  20. Pochet N, Smet FD, Suykens JAK, Moor BLRD (2004) Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction. Bioinformatics 20:3185–3195

    Article  Google Scholar 

  21. Lee JW, Lee JB, Park M, Song SH (2005) An extensive comparison of recent classification tools applied to microarray data. Comput Stat Data Anal 48:869–885

    Article  MathSciNet  MATH  Google Scholar 

  22. Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, New York

    Book  Google Scholar 

  23. Tan AC, Gilbert D (2003) Ensemble machine learning on gene expression data for cancer classification. Appl Bioinform 2:S75–S83

    Google Scholar 

  24. Cho SB, Ryu JW (2002) Classifying gene expression data of cancer using classifier ensemble with mutually exclusive features. Proc IEEE 90:1744–1753

    Article  Google Scholar 

  25. Cho SB, Won HH (2007) Cancer classification using ensemble of neural networks with multiple significant gene subsets. Appl Intell 26:243–250

    Article  Google Scholar 

  26. Won HH, Cho SB (2003) Neural network ensemble with negatively correlated features for cancer classification. Lect Notes Comput Sci 2714:1143–1150

    Article  Google Scholar 

  27. Hochbaum D, Shmoys DB (1985) A best possible heuristic for the k-center problem. Math Oper Res 10:180–184

    Article  MathSciNet  MATH  Google Scholar 

  28. Dasgupta S (2010) Hierarchical clustering with performance guarantees. In: Classification as a tool for research, studies in classification, data analysis, and knowledge organization, pp. 3–14. doi:10.1007/978-3-642-10745-0_1

  29. Gonzalez TF (1985) Clustering to minimize the maximum intercluster distance. Theoret Comput Sci 38:293–306

    Article  MathSciNet  MATH  Google Scholar 

  30. Cho SB, Park CH (2004) Speciated GA for optimal ensemble classifiers in DNA microarray classification. IEEE Congr Evolut Comput 590–597

  31. Kim KJ, Cho SB (2005) DNA gene expression classification with ensemble classifiers optimized by speciated genetic algorithm. In: First international conference on pattern recognition and machine intelligence, pp 649–653

  32. Park CH, Cho SB (2003) Evolutionary ensemble classifier for lymphoma and colon cancer classification. IEEE Congr Evolut Comput 2378–2385

  33. Park CH, Cho SB (2003) Evolutionary computation for optimal ensemble classifier in lymphoma cancer. In: 14th international symposium on methodologies for intelligent systems, pp 521–530

  34. Kim KJ, Cho SB (2010) Exploring features and classifiers to classify microRNA expression profiles of human cancer. In: 17th international conference on neural information processing, pp 234–241

  35. Xu L, Krzyzak A, Suen CY (1992) Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans Syst Man Cybern 22:418–435

    Article  Google Scholar 

  36. RANKGENE. http://genomics10.bu.edu/yangsu/rankgene/

  37. LIBSVM. http://www.csie.ntu.edu.tw/~cjlin/libsvm/

  38. Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D et al (1999) Broad patterns of gene expression revealed by clustering of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA 96:6745–6750

    Article  Google Scholar 

  39. Singh D, Febbo PG, Ross K, Jackson DG, Manola J, Ladd C et al (2002) Gene expression correlates of clinical prostate cancer behaviour. Cancer Cell 1:203–209

    Article  Google Scholar 

  40. van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536

    Article  Google Scholar 

  41. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A et al (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403:503–511

    Article  Google Scholar 

  42. Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, London

    Google Scholar 

  43. WEKA Toolkit. www.cs.waikato.ac.nz/ml/weka/

  44. Kim KJ, Cho SB (2006) Ensemble classifiers based on correlation analysis for DNA microarray classification. Neurocomputing 70:187–199

    Article  Google Scholar 

  45. Dehuri S, Roy R, Cho SB, Ghosh A (2012) An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. J Syst Softw 85:1333–1345

    Article  Google Scholar 

  46. Luo Y, Tao D, Geng Bo, Xu C, Maybank SJ (2013) Manifold regularized multitask learning for semi-supervised multilabel image classification. IEEE Trans Image Process 22:523–536

    Article  MathSciNet  Google Scholar 

  47. Luo Y, Tao D, Xu C, Xu C, Liu H, Wen Y (2013) Multiview vector-valued manifold regularization for multilabel image classification. IEEE Trans Neural Netw Learn Syst 24:709–722

    Article  Google Scholar 

  48. Hwang TH, Tian Z, Kuang R, Kocher JP (2008) Learning on weighted hypergraphs to integrate protein interactions and gene expressions for cancer outcome prediction. In: IEEE international conference on data mining, pp 293–302

  49. Tian Z, Hwang TH, Kuang R (2009) A hypergraph-based learning algorithm for classifying gene expression and array CGH data with prior knowledge. Bioinformatics 25:2831–2838

    Article  MATH  Google Scholar 

  50. Zhou D, Huang J, Scholkopf (2005) Learning from labeled and unlabeled data on a directed graph. In: Proceedings of the 22nd international conference on machine learning, pp 1036–1043

  51. Zhu X, Ghahramani Z, Lafferty J (2003) Semi-supervised learning using Gaussian fields and harmonic functions. In: Proceedings of the international conference on machine learning, pp 912–919

  52. Wu M, Scholkopf B (2007) Transductive classification via local learning regularization. J Mach Learn Res-Proc Track 2:628–635

    Google Scholar 

  53. Yu J, Tao D, Wang M (2012) Adaptive hypergraph learning and its application in image classification. IEEE Trans Image Process 21:3262–3272

    Article  MathSciNet  Google Scholar 

  54. Yu J, Wang M, Tao D (2012) Semisupervised multiview distance metric learning for cartoon synthesis. IEEE Trans Image Process 21:4636–4648

    Article  MathSciNet  Google Scholar 

  55. Yu J, Liu D, Tao D, Seah HS (2011) Complex object correspondence construction in two-dimensional animation. IEEE Trans Image Process 20:3257–3269

    Article  MathSciNet  Google Scholar 

  56. Tao D, Li X, Wu X, Maybank SJ (2007) General tensor discriminant analysis and Gabor features for gait recognition. IEEE Trans Pattern Anal Mach Intell 29:1700–1715

    Article  Google Scholar 

  57. Tao D, Li X, Wu X, Maybank SJ (2009) Geometric mean for subspace selection. IEEE Trans Pattern Anal Mach Intell 31:260–274

    Article  Google Scholar 

  58. Zhang T, Tao D, Li X, Yang J (2009) Patch alignment for dimensionality reduction. IEEE Trans Knowl Data Eng 21:1299–1313

    Article  Google Scholar 

  59. Yu J, Liu D, Tao D, Seah HS (2012) On combining multiple features for cartoon character retrieval and clip synthesis. IEEE Trans Syst Man Cybern––Part B: Cybern 42:1413–1427

    Article  Google Scholar 

  60. Yu J, Tao D (2013) Modern machine learning techniques and their applications in cartoon animation research, Wiley-IEEE Press, Piscataway

  61. Dhillon IS, Guan Y, Kulis B (2004) Kernel k-menas: Spectral clustering and normalized cuts. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp 551–556

  62. Pauca VP, Shahnaz F, Berry MW, Plemmons RJ (2004) Text mining using non-negative matrix factorizations. In: Proceedings of the fourth SIAM international conference on data mining, pp 452–456

  63. Guan N, Tao D, Luo Z, Yuan B (2011) Non-negative patch alignment framework. IEEE Trans Neural Netw 22:1218–1230

    Article  Google Scholar 

  64. Guan N, Tao D, Luo Z, Yuan B (2012) NeNMF: an optimal gradient method for nonnegative matrix factorization. IEEE Trans Signal Process 60:2882–2898

Download references

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (2013 R1A2A2A01016589, 2010-0018950, 2010-0018948).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Sejong University, Seoul, 143-747, South Korea

    Kyung-Joong Kim

  2. Department of Computer Science, Yonsei University, Seoul, South Korea

    Sung-Bae Cho

Authors
  1. Kyung-Joong Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sung-Bae Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kyung-Joong Kim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, KJ., Cho, SB. Meta-classifiers for high-dimensional, small sample classification for gene expression analysis. Pattern Anal Applic 18, 553–569 (2015). https://doi.org/10.1007/s10044-014-0369-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-014-0369-7

Keywords

Search

Navigation