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Erschienen in:
Fundamentals of Evolutionary Machine Learning Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

14. Genetic Programming as an Innovation Engine for Automated Machine Learning: The Tree-Based Pipeline Optimization Tool (TPOT)

verfasst von : Jason H. Moore, Pedro H. Ribeiro, Nicholas Matsumoto, Anil K. Saini

Erschienen in: Handbook of Evolutionary Machine Learning

Verlag: Springer Nature Singapore

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Abstract

One of the central challenges of machine learning is the selection of methods for feature selection, feature engineering, and classification or regression algorithms for building an analytics pipeline. This is true for both novices and experts. Automated machine learning (AutoML) has emerged as a useful approach to generate machine learning pipelines without the need for manual construction and evaluation. We review here some challenges of building pipelines and present several of the first and most widely used AutoML methods and open-source software. We present in detail the Tree-based Pipeline Optimization Tool (TPOT) that represents pipelines as expression trees and uses genetic programming (GP) for discovery and optimization. We present some of the extensions of TPOT and its application to real-world big data. We end with some thoughts about the future of AutoML and evolutionary machine learning.

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Vorheriges Kapitel Adversarial Evolutionary Learning with Distributed Spatial Coevolution
Nächstes Kapitel Evolutionary Model Validation—An Adversarial Robustness Perspective
Literatur
1.
Chicco, D., Oneto, L., Tavazzi, E.: Eleven quick tips for data cleaning and feature engineering. PLoS Comput. Biol. 18, e1010718 (2022)CrossRef
2.
Urbanowicz, R.J., Meeker, M., La Cava, W., Olson, R.S., Moore, J.H.: Relief-based feature selection: introduction and review. J. Biomed. Inform. 85, 189–203 (2018)CrossRef
3.
Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38 (2006)
4.
Smits, G.F., Kotanchek, M.: Pareto-front exploitation in symbolic regression. In: O’Reilly, U.-M., Yu, T., Riolo, R., Worzel, B. (eds.) Genetic Programming Theory and Practice II. pp. 283–299 (2006)
5.
Combi, C., Amico, B., Bellazzi, R., Holzinger, A., Moore, J.H., Zitnik, M., Holmes, J.H.: A manifesto on explainability for artificial intelligence in medicine. Artif. Intell. Med. 133, 102423 (2022)CrossRef
6.
Hutter, F., Kotthoff, L., Vanschoren, J. (eds.): Automated Machine Learning: Methods, Systems, Challenges. Springer International Publishing (2019)
7.
Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 847–855. ACM, New York, NY, USA (2013)
8.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11, 10–18 (2009)CrossRef
9.
Wang, H.-L., Hsu, W.-Y., Lee, M.-H., Weng, H.-H., Chang, S.-W., Yang, J.-T., Tsai, Y.-H.: Automatic machine-learning-based outcome prediction in patients with primary intracerebral hemorrhage. Front. Neurol. 10, 910 (2019)CrossRef
10.
Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28. pp. 2962–2970. Curran Associates, Inc. (2015)
11.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH
12.
Howard, D., Maslej, M.M., Lee, J., Ritchie, J., Woollard, G., French, L.: Transfer learning for risk classification of social media posts: model evaluation study. J. Med. Internet Res. 22, e15371 (2020)CrossRef
13.
Olson, R.S., Urbanowicz, R.J., Andrews, P.C., Lavender, N.A., Kidd, L.C., Moore, J.H.: Automating biomedical data science through tree-based pipeline optimization. In: Squillero, G., Burelli, P. (eds.) Applications of Evolutionary Computation, pp. 123–137. Springer International Publishing, Cham (2016)CrossRef
14.
Olson, R.S., Bartley, N., Urbanowicz, R.J., Moore, J.H.: Evaluation of a tree-based pipeline optimization tool for automating data science. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, pp. 485–492. ACM, New York, NY, USA (2016)
15.
Olson, R.S., Moore, J.H.: TPOT: a tree-based pipeline optimization tool for automating machine learning. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Automated Machine Learning: Methods, Systems, Challenges, pp. 151–160. Springer International Publishing, Cham (2019)CrossRef
16.
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA (1992)MATH
17.
Fortin, F., De Rainville, F., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)MathSciNet
18.
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)CrossRef
19.
Helmuth, T., McPhee, N.F., Spector, L.: Lexicase selection for program synthesis: a diversity analysis. In: Riolo, R., Worzel, W.P., Kotanchek, M., Kordon, A. (eds.) Genetic Programming Theory and Practice XIII, pp. 151–167. Springer International Publishing, Cham (2016)CrossRef
20.
Le, T.T., Fu, W., Moore, J.H.: Scaling tree-based automated machine learning to biomedical big data with a feature set selector. Bioinforma. Oxf. Engl. 36, 250–256 (2020)CrossRef
21.
Romano, J., Le, T., Fu, W., Moore, J.: TPOT-NN: augmenting tree-based automated machine learning with neural network estimators. Genet. Program. Evolvable Mach. 1–21 (2021)
22.
Manduchi, E., Romano, J.D., Moore, J.H.: The promise of automated machine learning for the genetic analysis of complex traits. Hum. Genet. 141, 1529–1544 (2022)CrossRef
23.
Orlenko, A., Kofink, D., Lyytikäinen, L.-P., Nikus, K., Mishra, P., Kuukasjärvi, P., Karhunen, P.J., Kähönen, M., Laurikka, J.O., Lehtimäki, T., Asselbergs, F.W., Moore, J.H.: Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning. Bioinforma. Oxf. Engl. 36, 1772–1778 (2020)CrossRef
24.
Manduchi, E., Fu, W., Romano, J.D., Ruberto, S., Moore, J.H.: Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses. BMC Bioinform. 21, 430 (2020)CrossRef
25.
Purkayastha, S., Zhao, Y., Wu, J., Hu, R., McGirr, A., Singh, S., Chang, K., Huang, R.Y., Zhang, P.J., Silva, A., Soulen, M.C., Stavropoulos, S.W., Zhang, Z., Bai, H.X.: Differentiation of low and high grade renal cell carcinoma on routine MRI with an externally validated automatic machine learning algorithm. Sci. Rep. 10, 19503 (2020)CrossRef
26.
Heimisdottir, L.H., Lin, B.M., Cho, H., Orlenko, A., Ribeiro, A.A., Simon-Soro, A., Roach, J., Shungin, D., Ginnis, J., Simancas-Pallares, M.A., Spangler, H.D., Zandoná, A.G.F., Wright, J.T., Ramamoorthy, P., Moore, J.H., Koo, H., Wu, D., Divaris, K.: Metabolomics insights in early childhood caries. J. Dent. Res. 100, 615–622 (2021)CrossRef
27.
Manduchi, E., Le, T.T., Fu, W., Moore, J.H.: Genetic analysis of coronary artery disease using tree-based automated machine learning informed by biology-based feature selection. IEEE/ACM Trans. Comput. Biol. Bioinform. 19, 1379–1386 (2022)CrossRef
28.
Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems. Curran Associates, Inc. (2017)
29.
Sipper, M., Moore, J.H.: Genetic programming theory and practice: a fifteen-year trajectory. Genet. Program Evolvable Mach. 21, 169–179 (2020)CrossRef
30.
La Cava, W., Williams, H., Fu, W., Vitale, S., Srivatsan, D., Moore, J.H.: Evaluating recommender systems for AI-driven biomedical informatics. Bioinforma. Oxf. Engl. 37, 250–256 (2021)CrossRef
31.
Moore, J.H., Parker, J.S., Olsen, N.J., Aune, T.M.: Symbolic discriminant analysis of microarray data in autoimmune disease. Genet. Epidemiol. 23, 57–69 (2002)CrossRef
Metadaten
Titel
Genetic Programming as an Innovation Engine for Automated Machine Learning: The Tree-Based Pipeline Optimization Tool (TPOT)
verfasst von
Jason H. Moore
Pedro H. Ribeiro
Nicholas Matsumoto
Anil K. Saini
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Handbook of Evolutionary Machine Learning

Print ISBN: 978-981-9938-13-1

Electronic ISBN: 978-981-9938-14-8

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-99-3814-8_14

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