Skip to main content
SpringerLink
Log in
Book cover

International Conference on Discovery Science

DS 2018: Discovery Science pp 144–158Cite as

Predicting Rice Phenotypes with Meta-learning

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11198))

Abstract

The features in some machine learning datasets can naturally be divided into groups. This is the case with genomic data, where features can be grouped by chromosome. In many applications it is common for these groupings to be ignored, as interactions may exist between features belonging to different groups. However, including a group that does not influence a response introduces noise when fitting a model, leading to suboptimal predictive accuracy. Here we present two general frameworks for the generation and combination of meta-features when feature groupings are present. We evaluated the frameworks on a genomic rice dataset where the regression task is to predict plant phenotype. We conclude that there are use cases for both frameworks.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Alexandrov, N., et al.: SNP-seek database of SNPs derived from 3000 rice genomes. Nucl. Acids Res. 43(D1), D1023–D1027 (2015)

    Article  Google Scholar 

  2. Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)

    MathSciNet  Google Scholar 

  3. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(Feb), 281–305 (2012)

    Google Scholar 

  4. Breheny, P., Huang, J.: Penalized methods for bi-level variable selection. Stat. Interface 2(3), 369 (2009)

    Article  MathSciNet  Google Scholar 

  5. Breiman, L.: Stacked regressions. Mach. Learn. 24(1), 49–64 (1996)

    MathSciNet  MATH  Google Scholar 

  6. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  7. Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A.: Ensemble selection from libraries of models. In: Proceedings of the Twenty-first International Conference on Machine Learning, p. 18. ACM (2004)

    Google Scholar 

  8. Chen, T., He, T.: xgboost: extreme gradient boosting. R package version 0.4-2 (2015)

    Google Scholar 

  9. Cortes, C., Mohri, M., Rostamizadeh, A.: Learning non-linear combinations of kernels. In: Advances in Neural Information Processing Systems, pp. 396–404 (2009)

    Google Scholar 

  10. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  11. Džeroski, S., Ženko, B.: Stacking with multi-response model trees. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, vol. 2364, pp. 201–211. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45428-4_20

    Chapter  Google Scholar 

  12. Džeroski, S., Ženko, B.: Is combining classifiers with stacking better than selecting the best one? Mach. Learn. 54(3), 255–273 (2004)

    Article  Google Scholar 

  13. Endelman, J.B.: Ridge regression and other kernels for genomic selection with r package rrBLUP. Plant Genome 4(3), 250–255 (2011)

    Article  Google Scholar 

  14. Friedman, J., Hastie, T., Tibshirani, R.: A note on the group lasso and a sparse group lasso. arXiv preprint arXiv:1001.0736 (2010)

  15. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)

    Google Scholar 

  16. Grenier, C., et al.: Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding. PloS ONE 10(8), e0136594 (2015)

    Article  Google Scholar 

  17. Grinberg, N.F., et al.: Implementation of genomic prediction in Lolium perenne (L.) breeding populations. Front. Plant Sci. 7, 133 (2016)

    Google Scholar 

  18. Hainmueller, J., Hazlett, C.: Kernel regularized least squares: Reducing misspecification bias with a flexible and interpretable machine learning approach. Polit. Anal. mpt019 (2013)

    Google Scholar 

  19. Huang, J., Ma, S., Xie, H., Zhang, C.H.: A group bridge approach for variable selection. Biometrika 96(2), 339–355 (2009)

    Article  MathSciNet  Google Scholar 

  20. Ihaka, R., Gentleman, R.: R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5(3), 299–314 (1996)

    Google Scholar 

  21. Jahrer, M., Töscher, A., Legenstein, R.: Combining predictions for accurate recommender systems. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 693–702. ACM (2010)

    Google Scholar 

  22. Jolliffe, I.T.: A note on the use of principal components in regression. Appl. Stat. 31(3) 300–303 (1982)

    Article  Google Scholar 

  23. Kivinen, J., Warmuth, M.K.: Exponentiated gradient versus gradient descent for linear predictors. Inf. Comput. 132(1), 1–63 (1997)

    Article  MathSciNet  Google Scholar 

  24. Van der Laan, M.J., Polley, E.C., Hubbard, A.E.: Super learner. Stat. Appl. Genet. Mol. Biol. 6(1), 1544–6115 (2007)

    Google Scholar 

  25. Lanckriet, G.R., Cristianini, N., Bartlett, P., Ghaoui, L.E., Jordan, M.I.: Learning the kernel matrix with semidefinite programming. J. Mach. Learn. Res. 5(Jan), 27–72 (2004)

    Google Scholar 

  26. Maclean, J., Hardy, B., Hettel, G.: Rice almanac: source book for one of the most important economic activities on earth. In: IRRI (2013)

    Google Scholar 

  27. Mendes-Moreira, J., Soares, C., Jorge, A.M., Sousa, J.F.D.: Ensemble approaches for regression: a survey. ACM Comput. Surv. (CSUR) 45(1), 10 (2012)

    Article  Google Scholar 

  28. Merz, C.J.: Classification and regression by combining models. Ph.D. thesis, University of California Irvine (1998)

    Google Scholar 

  29. Ni, W., Brown, S.D., Man, R.: Stacked partial least squares regression analysis for spectral calibration and prediction. J. Chemom. 23(10), 505–517 (2009)

    Article  Google Scholar 

  30. Ogutu, J.O., Piepho, H.P.: Regularized group regression methods for genomic prediction: Bridge, MCP, SCAD, group bridge, group lasso, sparse group lasso, group MCP and group SCAD. In: BMC Proceedings. vol. 8, p. S7. BioMed Central (2014)

    Article  Google Scholar 

  31. Onogi, A., et al..: Exploring the areas of applicability of whole-genome prediction methods for asian rice (oryza sativa l.). Theor. Appl. Genet. 128(1), 41–53 (2015)

    Article  Google Scholar 

  32. Parmanto, B., Munro, P.W., Doyle, H.R.: Reducing variance of committee prediction with resampling techniques. Connect. Sci. 8(3–4), 405–426 (1996)

    Article  Google Scholar 

  33. Purcell, S., et al.: Plink: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81(3), 559–575 (2007)

    Article  Google Scholar 

  34. Ray, D.K., Mueller, N.D., West, P.C., Foley, J.A.: Yield trends are insufficient to double global crop production by 2050 (2013)

    Article  Google Scholar 

  35. Rooney, N., Patterson, D., Anand, S., Tsymbal, A.: Dynamic integration of regression models. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 164–173. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25966-4_16

    Chapter  Google Scholar 

  36. Rutkoski, J.E., Poland, J., Jannink, J., Sorrells, M.E.: Imputation of unordered markers and the impact on genomic selection accuracy. G3: Genes Genomes Genet. 3(3), 427–439 (2013)

    Article  Google Scholar 

  37. Sonnenburg, S., Rätsch, G., Schäfer, C., Schölkopf, B.: Large scale multiple kernel learning. J. Mach. Learn. Res. 7(Jul), 1531–1565 (2006)

    Google Scholar 

  38. Spindel, J., et al.: Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet. 11(2), e1004982 (2015)

    Article  Google Scholar 

  39. Tai, A.P., Martin, M.V., Heald, C.L.: Threat to future global food security from climate change and ozone air pollution. Nat. Clim. Chang. 4(9), 817–821 (2014)

    Article  Google Scholar 

  40. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Society. Ser. B (Methodol.) 58(1) 267–288 (1996)

    Google Scholar 

  41. Ting, K.M., Witten, I.H.: Issues in stacked generalization. J. Artif. Intell. Res. (JAIR) 10, 271–289 (1999)

    Article  Google Scholar 

  42. Un, U.N.: World population prospects: the 2015 revision, key findings and advance tables. Working Paper, No. ESA/P/WP. 241. (2015)

    Google Scholar 

  43. Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. arXiv preprint arXiv:1304.5634 (2013)

  44. Xu, L., Jiang, J.H., Zhou, Y.P., Wu, H.L., Shen, G.L., Yu, R.Q.: MCCV stacked regression for model combination and fast spectral interval selection in multivariate calibration. Chemom. Intell. Lab. Syst. 87(2), 226–230 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The University of Manchester, Manchester, M13 9PL, United Kingdom

    Oghenejokpeme I. Orhobor & Ross D. King

  2. The International Rice Research Institute, Los Baños, Philippines

    Nickolai N. Alexandrov

Authors
  1. Oghenejokpeme I. Orhobor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nickolai N. Alexandrov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ross D. King
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oghenejokpeme I. Orhobor .

Editor information

Editors and Affiliations

  1. Goldsmiths University of London, London, UK

    Larisa Soldatova

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Joaquin Vanschoren

  3. University of Cyprus, Nicosia, Cyprus

    George Papadopoulos

  4. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Orhobor, O.I., Alexandrov, N.N., King, R.D. (2018). Predicting Rice Phenotypes with Meta-learning. In: Soldatova, L., Vanschoren, J., Papadopoulos, G., Ceci, M. (eds) Discovery Science. DS 2018. Lecture Notes in Computer Science(), vol 11198. Springer, Cham. https://doi.org/10.1007/978-3-030-01771-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01771-2_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01770-5

  • Online ISBN: 978-3-030-01771-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation