References
[1]. D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, p.484, 2016.
[2]. M. Feurer and F. Hutter, “Hyperparameter optimization,” 2018.[Online]. Available: https://www.ml4aad.org/wp-content/uploads/2018/09/chapter1-hpo.pdf.
[3]. G. Katz, E. C. R. Shin, and D. Song, “Explorekit: Automatic feature generation and selection,” in International Conference on Data Mining, 2016, pp. 979–984.
[4]. J. M. Kanter and K. Veeramachaneni, “Deep feature synthesis: Towards automating data science endeavors,” in IEEE International Conference on Data Science and Advanced Analytics, 2015, pp.1–10.
[5]. G. Katz, E. C. R. Shin, and D. Song, “Explorekit: Automatic feature generation and selection,” in International Conference on Data Mining, 2016, pp. 979–984.
[6]. J. M. Kanter and K. Veeramachaneni, “Deep feature synthesis: Towards automating data science endeavors,” in IEEE International Conference on Data Science and Advanced Analytics, 2015, pp.1–10.
[7]. B. Tran, B. Xue, and M. Zhang, “Genetic programming for feature construction and selection in classification on high-dimensional data,” Memetic Computing, vol. 8, no. 1, pp. 3–15, 2016
[8]. G. Katz, E. C. R. Shin, and D. Song, “Explorekit: Automatic feature generation and selection,” in International Conference on Data Mining, 2016, pp. 979–984.
[9]. K. Swersky, J. Snoek, and R. P. Adams, “Freeze-thaw bayesian optimization,” arXiv preprint arXiv:1406.3896, 2014.
[10]. T. Nickson, M. A. Osborne, S. Reece, and S. J. Roberts, “Automated machine learning on big data using stochastic algorithm tuning,” arXiv preprint arXiv:1407.7969, 2014.
[11]. C. Thornton, F. Hutter, H. Hoos, and K. Leyton-Brown, “AutoWEKA: Combined selection and hyperparameter optimization of classification algorithms,” in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013, pp.847–855.
[12]. M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum, and F. Hutter, “Efficient and robust automated machine learning,” in Advances in Neural Information Processing Systems,2015, pp. 2962–2970.
[13]. L. Kotthoff, C. Thornton, H. Hoos, F. Hutter, and K. LeytonBrown, “Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA,” Journal of Machine Learning Research, vol. 18, no. 1, pp. 826–830, 2017.
[14]. J. A. Tropp, “Greed is good: Algorithmic results for sparse approximation,” IEEE Transactions on Information theory, vol. 50,no. 10, pp. 2231–2242, 2004.
[15]. S. Huang, X. Li, Z. Cheng, Z. Zhang, and A. G. Hauptmann,“GNAS: A greedy neural architecture search method for multiattribute learning,” in ACM Multimedia, 2018, pp. 2049–2057.
[16]. K. Eggensperger, F. Hutter, H. H. Hoos, and K. Leyton-Brown,“Efficient benchmarking of hyperparameter optimizers via surrogates,” in AAAI Conference on Artificial Intelligence, 2015.
[17]. J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” Journal of Machine Learning Research, vol. 13, no. Feb, pp. 281–305, 2012.
[18]. Guo Y, Huang J, Dong Y et al. Guoym at SemEval-2020 task 8: Ensemble-based classification of visuo-lingual metaphor in memes. In Proceedings of the Fourteenth Workshop on Semantic Evaluation. Barcelona (online): International Committee for Computational Linguistics, pp. 1120–1125. URL https://aclanthology.org/2020.semeval-1.148.
Cite this article
Jin,Z. (2023). Exploring the Advancements and Challenges of Automated Machine Learning. Applied and Computational Engineering,8,847-852.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, p.484, 2016.
[2]. M. Feurer and F. Hutter, “Hyperparameter optimization,” 2018.[Online]. Available: https://www.ml4aad.org/wp-content/uploads/2018/09/chapter1-hpo.pdf.
[3]. G. Katz, E. C. R. Shin, and D. Song, “Explorekit: Automatic feature generation and selection,” in International Conference on Data Mining, 2016, pp. 979–984.
[4]. J. M. Kanter and K. Veeramachaneni, “Deep feature synthesis: Towards automating data science endeavors,” in IEEE International Conference on Data Science and Advanced Analytics, 2015, pp.1–10.
[5]. G. Katz, E. C. R. Shin, and D. Song, “Explorekit: Automatic feature generation and selection,” in International Conference on Data Mining, 2016, pp. 979–984.
[6]. J. M. Kanter and K. Veeramachaneni, “Deep feature synthesis: Towards automating data science endeavors,” in IEEE International Conference on Data Science and Advanced Analytics, 2015, pp.1–10.
[7]. B. Tran, B. Xue, and M. Zhang, “Genetic programming for feature construction and selection in classification on high-dimensional data,” Memetic Computing, vol. 8, no. 1, pp. 3–15, 2016
[8]. G. Katz, E. C. R. Shin, and D. Song, “Explorekit: Automatic feature generation and selection,” in International Conference on Data Mining, 2016, pp. 979–984.
[9]. K. Swersky, J. Snoek, and R. P. Adams, “Freeze-thaw bayesian optimization,” arXiv preprint arXiv:1406.3896, 2014.
[10]. T. Nickson, M. A. Osborne, S. Reece, and S. J. Roberts, “Automated machine learning on big data using stochastic algorithm tuning,” arXiv preprint arXiv:1407.7969, 2014.
[11]. C. Thornton, F. Hutter, H. Hoos, and K. Leyton-Brown, “AutoWEKA: Combined selection and hyperparameter optimization of classification algorithms,” in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013, pp.847–855.
[12]. M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum, and F. Hutter, “Efficient and robust automated machine learning,” in Advances in Neural Information Processing Systems,2015, pp. 2962–2970.
[13]. L. Kotthoff, C. Thornton, H. Hoos, F. Hutter, and K. LeytonBrown, “Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA,” Journal of Machine Learning Research, vol. 18, no. 1, pp. 826–830, 2017.
[14]. J. A. Tropp, “Greed is good: Algorithmic results for sparse approximation,” IEEE Transactions on Information theory, vol. 50,no. 10, pp. 2231–2242, 2004.
[15]. S. Huang, X. Li, Z. Cheng, Z. Zhang, and A. G. Hauptmann,“GNAS: A greedy neural architecture search method for multiattribute learning,” in ACM Multimedia, 2018, pp. 2049–2057.
[16]. K. Eggensperger, F. Hutter, H. H. Hoos, and K. Leyton-Brown,“Efficient benchmarking of hyperparameter optimizers via surrogates,” in AAAI Conference on Artificial Intelligence, 2015.
[17]. J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” Journal of Machine Learning Research, vol. 13, no. Feb, pp. 281–305, 2012.
[18]. Guo Y, Huang J, Dong Y et al. Guoym at SemEval-2020 task 8: Ensemble-based classification of visuo-lingual metaphor in memes. In Proceedings of the Fourteenth Workshop on Semantic Evaluation. Barcelona (online): International Committee for Computational Linguistics, pp. 1120–1125. URL https://aclanthology.org/2020.semeval-1.148.