Skip to main content

Advertisement

SpringerLink
Log in

A review of automatic selection methods for machine learning algorithms and hyper-parameter values

  • Original Article
  • Published:
Network Modeling Analysis in Health Informatics and Bioinformatics Aims and scope Submit manuscript

Abstract

Machine learning studies automatic algorithms that improve themselves through experience. It is widely used for analyzing and extracting value from large biomedical data sets, or “big biomedical data,” advancing biomedical research, and improving healthcare. Before a machine learning model is trained, the user of a machine learning software tool typically must manually select a machine learning algorithm and set one or more model parameters termed hyper-parameters. The algorithm and hyper-parameter values used can greatly impact the resulting model’s performance, but their selection requires special expertise as well as many labor-intensive manual iterations. To make machine learning accessible to layman users with limited computing expertise, computer science researchers have proposed various automatic selection methods for algorithms and/or hyper-parameter values for a given supervised machine learning problem. This paper reviews these methods, identifies several of their limitations in the big biomedical data environment, and provides preliminary thoughts on how to address these limitations. These findings establish a foundation for future research on automatically selecting algorithms and hyper-parameter values for analyzing big biomedical data.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Adankon MM, Cheriet M (2009) Model selection for the LS-SVM. Application to handwriting recognition. Pattern Recognit 42(12):3264–3270

    Article  MATH  Google Scholar 

  • Ali A, Caruana R, Kapoor A (2014) Active learning with model selection. In: Proceedings of AAAI’14, pp 1673–1679

  • Alpaydin E (2014) Introduction to machine learning, 3rd edn. The MIT Press, Cambridge

    MATH  Google Scholar 

  • Bardenet R, Brendel M, Kégl B, Sebag M (2013) Collaborative hyperparameter tuning. In: Proceedings of ICML’13, pp 199–207

  • Bengio Y (2000) Gradient-based optimization of hyperparameters. Neural Comput 12(8):1889–1900

    Article  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  • Bergstra J, Bardenet R, Bengio Y, Kégl B (2011) Algorithms for hyper-parameter optimization. In: Proceedings of NIPS’11, pp 2546–2554

  • Bergstra J, Yamins D, Cox DD (2013) Hyperopt: a Python library for optimizing the hyperparameters of machine learning algorithms. In: Proceedings of SciPy 2013, pp 13–20

  • Bertsekas DP (1999) Nonlinear programming, 2nd edn. Athena Scientific, Belmont

    MATH  Google Scholar 

  • Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Brazdil P, Soares C, da Costa JP (2003) Ranking learning algorithms: using IBL and meta-learning on accuracy and time results. Mach Learn 50(3):251–277

    Article  MATH  Google Scholar 

  • Burnham KP, Anderson DR (2003) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New York

    MATH  Google Scholar 

  • Caruana R, Niculescu-Mizil A, Crew G, Ksikes A (2004) Ensemble selection from libraries of models. In: Proceedings of ICML’04

  • Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M et al. (2006) Bigtable: a distributed storage system for structured data. In: Proceedings of OSDI’06, pp 205–218

  • Claeskens G, Hjort N (2008) Model selection and model averaging. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Cleophas TJ, Zwinderman AH (2013a) Machine learning in medicine. Springer, New York

    Book  Google Scholar 

  • Cleophas TJ, Zwinderman AH (2013b) Machine learning in medicine: Part 2. Springer, New York

    Book  Google Scholar 

  • Cleophas TJ, Zwinderman AH (2013c) Machine learning in medicine: Part 3. Springer, New York

    Book  Google Scholar 

  • Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. In: Proceedings of OSDI’04, pp 137–150

  • Domhan T, Springenberg JT, Hutter F (2015) Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves. In: Proceedings of IJCAI’15, pp 3460–3468

  • Einbinder JS, Scully KW, Pates RD, Schubart JR, Reynolds RE (2001) Case study: a data warehouse for an academic medical center. J Healthc Inf Manag. 15(2):165–175

    Google Scholar 

  • Feurer M, Klein A, Eggensperger K, Springenberg J, Blum M, Hutter F (2015a) Efficient and robust automated machine learning. In: Proceedings of NIPS’15, pp 2944–2952

  • Feurer M, Springenberg T, Hutter F (2015b) Initializing Bayesian hyperparameter optimization via meta-learning. In: Proceedings of AAAI’15, pp 1128–1135

  • Fürnkranz J, Petrak J (2001) An evaluation of landmarking variants. In: Proceedings ECML/PKDD Workshop on Integrating Aspects of Data Mining, Decision Support and Meta-Learning 2001, pp 57–68

  • Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2013) Bayesian data analysis, 3rd edn. Chapman and Hall/CRC, Boca Raton

    MATH  Google Scholar 

  • Google Prediction API homepage (2016) https://cloud.google.com/prediction/docs. Accessed 20 January 2016

  • Gu B, Liu B, Hu F, Liu H (2001) Efficiently determining the starting sample size for progressive sampling. In: Proceedings of ECML’01, pp 192–202

  • Guo XC, Yang JH, Wu CG, Wang CY, Liang YC (2008) A novel LS-SVMs hyper-parameter selection based on particle swarm optimization. Neurocomputing 71(16–18):3211–3215

    Article  Google Scholar 

  • Guyon I, Bennett K, Cawley GC, Escalante HJ, Escalera S, Ho TK, Macià N, Ray B, Saeed M, Statnikov AR, Viegas E (2015) Design of the 2015 ChaLearn AutoML challenge. In: Proceedings of IJCNN’15, pp 1–8

  • Hendry DF, Doornik JA (2014) Empirical model discovery and theory evaluation: automatic selection methods in econometrics. The MIT Press, Cambridge

    Book  Google Scholar 

  • Hoffman MD, Shahriari B, de Freitas N (2014) On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning. In: Proceedings of AISTATS’14, pp 365–374

  • Hutter F, Hoos HH, Leyton-Brown K, Stützle T (2009) ParamILS: an automatic algorithm configuration framework. J Artif Intell Res 36:267–306

    MATH  Google Scholar 

  • Hutter F, Hoos HH, Leyton-Brown K (2011) Sequential model-based optimization for general algorithm configuration. In: Proceedings of LION’11, pp 507–523

  • Hutter F, Hoos H, Leyton-Brown K (2014) An efficient approach for assessing hyperparameter importance. In: Proceedings of ICML’14, pp 754–762

  • John GH, Langley P (1996) Static versus dynamic sampling for data mining. In: Proceedings of KDD’96, pp 367–370

  • Jovic A, Brkic K, Bogunovic N (2014) An overview of free software tools for general data mining. In: Proceedings of MIPRO’14, pp 1112–1117

  • Kadane JB, Lazar NA (2004) Methods and criteria for model selection. J Am Stat Assoc 99(465):279–290

    Article  MathSciNet  MATH  Google Scholar 

  • Komer B, Bergstra J, Eliasmith C (2014) Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn. In: Proceedings of SciPy 2014, pp 33–39

  • Kraska T, Talwalkar A, Duchi JC, Griffith R, Franklin MJ, Jordan MI (2013) MLbase: a distributed machine-learning system. In: Proceedings of CIDR’13

  • Lacoste A, Larochelle H, Marchand M, Laviolette F (2014a) Sequential model-based ensemble optimization. In: Proceedings of UAI’14, pp 440–448

  • Lacoste A, Marchand M, Laviolette F, Larochelle H (2014b) Agnostic Bayesian learning of ensembles. In: Proceedings of ICML’14, pp 611–619

  • Leite R, Brazdil P (2005) Predicting relative performance of classifiers from samples. In: Proceedings of ICML’05, pp 497–503

  • Leite R, Brazdil P (2010) Active testing strategy to predict the best classification algorithm via sampling and metalearning. In: Proceedings of ECAI’10, pp 309–314

  • Leite R, Brazdil P, Vanschoren J (2012) Selecting classification algorithms with active testing. In: Proceedings of MLDM’12, pp 117–131

  • Liu H, Motoda H (2013) Feature selection for knowledge discovery and data mining. Springer, New York

    MATH  Google Scholar 

  • Luo G (2015) MLBCD: a machine learning tool for big clinical data. Health Inf Sci Syst 3:3

    Article  Google Scholar 

  • Luo G (2016) Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction. Health Inf Sci Syst 4:2

    Article  Google Scholar 

  • Luo G, Frey LJ (2016) Efficient execution methods of pivoting for bulk extraction of Entity–Attribute–Value-modeled data. IEEE J Biomed Health Inform. 20(2):644–654

    Article  Google Scholar 

  • Luo G, Nkoy FL, Gesteland PH, Glasgow TS, Stone BL (2014) A systematic review of predictive modeling for bronchiolitis. Int J Med Inform 83(10):691–714

    Article  Google Scholar 

  • Luo G, Nkoy FL, Stone BL, Schmick D, Johnson MD (2015a) A systematic review of predictive models for asthma development in children. BMC Med Inform Decis Mak 15(1):99

    Article  Google Scholar 

  • Luo G, Stone BL, Sakaguchi F, Sheng X, Murtaugh MA (2015b) Using computational approaches to improve risk-stratified patient management: rationale and methods. JMIR Res Protoc. 4(4):e128

    Article  Google Scholar 

  • Luo G, Stone BL, Johnson MD, Nkoy FL (2016) Predicting appropriate admission of bronchiolitis patients in the emergency room: rationale and methods. JMIR Res Protoc. 5(1):e41

    Article  Google Scholar 

  • Maron O, Moore AW (1993) Hoeffding races: accelerating model selection search for classification and function approximation. In: Proceedings of NIPS’93, pp 59–66

  • Nadkarni PM (2011) Metadata-driven software systems in biomedicine: designing systems that can adapt to changing knowledge. Springer, New York

    Book  Google Scholar 

  • Nocedal J, Wright S (2006) Numerical optimization, 2nd edn. Springer, New York

    MATH  Google Scholar 

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  • Petrak J (2000) Fast subsampling performance estimates for classification algorithm selection. In: Proceedings of the ECML Workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination 2000, pp 3–14

  • Pfahringer B, Bensusan H, Giraud-Carrier CG (2000) Meta-learning by landmarking various learning algorithms. In: Proceedings of ICML’00, pp 743–750

  • Provost FJ, Jensen D, Oates T (1999) Efficient progressive sampling. In: Proceedings of KDD’99, pp 23–32

  • Roski J, Bo-Linn GW, Andrews TA (2014) Creating value in health care through big data: opportunities and policy implications. Health Aff (Millwood) 33(7):1115–1122

    Article  Google Scholar 

  • Sabharwal A, Samulowitz H, Tesauro G (2016) Selecting near-optimal learners via incremental data allocation. In: Proceedings of AAAI’16

  • Shahriari B, Swersky K, Wang Z, Adams RP, de Freitas N (2015) Taking the human out of the loop: a review of Bayesian optimization. Proc IEEE 104(1):148–175

    Article  Google Scholar 

  • Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. In: Proceedings of NIPS’12, pp 2960–2968

  • Soares C, Petrak J, Brazdil P (2001) Sampling-based relative landmarks: systematically test-driving algorithms before choosing. In: Proceedings of EPIA’01, pp 88–95

  • Sparks ER, Talwalkar A, Smith V, Kottalam J, Pan X, Gonzalez JE et al. (2013) MLI: an API for distributed machine learning. In: Proceedings of ICDM’13, pp 1187–1192

  • Sparks ER, Talwalkar A, Haas D, Franklin MJ, Jordan MI, Kraska T (2015) Automating model search for large scale machine learning. In: Proceedings of SoCC’15, pp 368–380

  • Steyerberg EW (2009) Clinical prediction models: a practical approach to development, validation, and updating. Springer, New York

    Book  MATH  Google Scholar 

  • Swersky K, Snoek J, Adams RP (2013) Multi-task Bayesian optimization. In: Proceedings of NIPS’13, 2004–2012

  • Swersky K, Snoek J, Adams RP (2014) Freeze-thaw Bayesian optimization. http://arxiv.org/abs/1406.3896. Accessed 20 January 2016

  • Thornton C, Hutter F, Hoos HH, Leyton-Brown K (2013) Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of KDD’13, pp 847–855

  • van Rijn JN, Abdulrahman SM, Brazdil P, Vanschoren J (2015) Fast algorithm selection using learning curves. In: Proceedings of IDA’15, pp 298–309

  • Wang L, Feng M, Zhou B, Xiang B, Mahadevan S (2015) Efficient hyper-parameter optimization for NLP applications. In: Proceedings of EMNLP’15, 2112–2117

  • White JM (2013) Bandit algorithms for website optimization. O’Reilly Media, Sebastopol

    Google Scholar 

  • Wistuba M, Schilling N, Schmidt-Thieme L (2015a) Hyperparameter search space pruning—a new component for sequential model-based hyperparameter optimization. In: Proceedings of ECML/PKDD (2) 2015, pp 104–119

  • Wistuba M, Schilling N, Schmidt-Thieme L (2015b) Learning hyperparameter optimization initializations. In: Proceedings of DSAA’15, pp 1–10

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

    Google Scholar 

  • Yogatama D, Mann G (2014) Efficient transfer learning method for automatic hyperparameter tuning. In: Proceedings of AISTATS’14, pp 1077–1085

  • Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I (2010) Spark: cluster computing with working sets. In: Proceedings of HotCloud 2010

  • Zhou Z (2012) Ensemble methods: foundations and algorithms. Chapman and Hall/CRC, Boca Raton

    Google Scholar 

Download references

Acknowledgments

We thank Qing T. Zeng, Michael Conway, Philip J. Brewster, David E. Jones, Angela P. Presson, Yue Zhang, Tom Greene, Alun Thomas, and Selena B. Thomas for helpful discussions.

Author information

Authors and Affiliations

  1. Department of Biomedical Informatics, University of Utah, Suite 140, 421 Wakara Way, Salt Lake City, UT, 84108, USA

    Gang Luo

Authors
  1. Gang Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gang Luo.

Ethics declarations

Conflict of interest

The author reports no conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luo, G. A review of automatic selection methods for machine learning algorithms and hyper-parameter values. Netw Model Anal Health Inform Bioinforma 5, 18 (2016). https://doi.org/10.1007/s13721-016-0125-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13721-016-0125-6

Keywords

Search

Navigation