Software:Auto-WEKA

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Auto-WEKA is an automated machine learning system based on Weka by Chris Thornton, Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown.[1] An extended version was published as Auto-WEKA 2.0.[2] Auto-WEKA was named the first prominent AutoML system in a neutral comparison study.[3]

It received the test-of-time award of the SIGKDD conference in 2023.[4]

Description

Auto-WEKA introduced the Combined Algorithm Selection and Hyperparameter optimization (CASH) problem, that extends both the Algorithm selection problem and the Hyperparameter optimization problem, by searching for the best algorithm and also its hyperparameters for a given dataset. Baratchi et al. state that "[T]he real power of AutoML was unlocked through the definition of the combined algorithm selection and hyperparameter optimisation problem".[5]

The CASH for formalism was picked up and also extended by later AutoML systems and methods such as Auto-sklearn,[6] ATM,[7] AutoPrognosis,[8] MCPS,[9] MOSAIC,[10] naive AutoML[11] and ADMM.[12]

References

  1. ↑ Thornton, Chris; Hutter, Frank; Hoos, Holger H.; Leyton-Brown, Kevin (August 11, 2013). "Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms". Association for Computing Machinery. pp. 847–855. doi:10.1145/2487575.2487629. https://doi.org/10.1145/2487575.2487629. 
  2. ↑ Kotthoff, Lars; Thornton, Chris; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin (August 12, 2017). "Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA". Journal of Machine Learning Research 18 (25): 1–5. http://jmlr.org/papers/v18/16-261.html. 
  3. ↑ Gijsbers, Pieter; Bueno, Marcos L. P. (2024). "AMLB: an AutoML Benchmark". Journal of Machine Learning Research 25: 6. http://jmlr.org/papers/v25/22-0493.html. 
  4. ↑ "KDD 2023 - Awards". https://www.kdd.org/kdd2023/awards/index.html. 
  5. ↑ Baratchi, Mitra; Wang, Can; Limmer, Steffen; van Rijn, Jan N.; Hoos, Holger; Bäck, Thomas; Olhofer, Thomas (2024). "Automated machine learning: past, present and future". Artificial Intelligence Review 57 (5): 2. doi:10.1007/s10462-024-10726-1. 
  6. ↑ Feurer, Matthias; Klein, Aaron; Eggensperger, Katharina; Springenberg, Jost Tobias; Blum, Manuel; Hutter, Frank (2015). "Efficient and Robust Automated Machine Learning". 28. http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning. 
  7. ↑ Swearingen, Thomas; Drevo, Will; Cyphers, Benett; Cuesta-Infante, Alfredo; Ross, Arun; Veeramachaneni, Kalyan (2017). "ATM: A distributed, collaborative, scalable system for automated machine learning". doi:10.1109/BigData.2017.8257923. 
  8. ↑ Alaa, Ahmed M.; van der Schaar, Mihaela (2018). "AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning". https://proceedings.mlr.press/v80/alaa18b.html. 
  9. ↑ Salvador, Manuel Martin; Budka, Marcin; Gabrys, Bogdan (2019). "Automatic Composition and Optimization of Multicomponent Predictive Systems With an Extended Auto-WEKA". IEEE Transactions on Automation Science and Engineering 16 (2): 946–959. doi:10.1109/TASE.2018.2876430. Bibcode: 2019ITASE..16..946M. 
  10. ↑ Rakotoarison, Herilalaina; Schoenauer, Marc; Sebag, Michèle (2019). "Automated Machine Learning with Monte-Carlo Tree Search". doi:10.24963/ijcai.2019/457. https://doi.org/10.24963/ijcai.2019/457. 
  11. ↑ Mohr, Felix; Wever, Marcel (2023). "Naive automated machine learning". Machine Learning 112 (4): 1131–1170. doi:10.1007/s10994-022-06200-0. 
  12. ↑ Liu, Sijia; Ram, Parikshit; Vijaykeerthy, Deepak; Bouneffouf, Djallel; Bramble, Gregory; Samulowitz, Horst; Wang, Dakuo; Conn, Andrew et al. (2020). "An ADMM based framework for automl pipeline configuration". 34. doi:10.1609/aaai.v34i04.5926. https://doi.org/10.1609/aaai.v34i04.5926. 




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