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AutomaticAI – A hybrid approach for automatic artificial intelligence algorithm selection and hyperparameter tuning.

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Title: AutomaticAI – A hybrid approach for automatic artificial intelligence algorithm selection and hyperparameter tuning.
Authors: Czako, Zoltan1 (AUTHOR) zoltan.czako@cs.utcluj.ro, Sebestyen, Gheorghe1 (AUTHOR) gheorghe.sebestyen@cs.utcluj.ro, Hangan, Anca1 (AUTHOR) anca.hangan@cs.utcluj.ro
Source: Expert Systems with Applications. Nov2021, Vol. 182, pN.PAG-N.PAG. 1p.
Subjects: Artificial intelligence, Algorithms, Particle swarm optimization, Problem solving, Simulated annealing
Abstract: • Automatic Artificial Intelligence Algorithm Selection. • Automatic Hyperparameter Optimization. • Particle Swarm Optimization which can handle continuous and discrete values. • Particle Swarm Optimization with Simulated Annealing acceptance criteria. • Global Optimal Solution. Recently, more and more real life problems are solved using artificial intelligence (AI) algorithms. One of the biggest challenges when working with AI is the selection and tuning of the best algorithm for solving the problem. The results generated by a given AI algorithm heavily depend on the way in which its hyperparameters are set. In most cases the process of algorithm selection and tuning is a manual, time consuming process in which the developer, based on experience and intuition tries to find the best solution from quality and execution time perspective. In this paper we present a method for solving the problem of AI algorithm selection and tuning, without human intervention, in a fully automated way. The method is a hybrid approach, a combination between particle swarm optimization and simulated annealing. We compare our approach with other similar tools like Auto-sklearn or Hyperopt-sklearn. We demonstrate the time efficiency and high accuracy of this method with some experiments on some known datasets. The paper also presents a platform for AI processing that include a set of procedures and services necessary in case of automatic processing of big datasets as well as the method for AI algorithm selection and tuning. This platform is useful for researchers and developers in an incipient phase of application development, when the best solution must be decided; it is also useful for specialists in different domains (physics, industry, economy) with less experience in using AI algorithms, but which has to process huge amount of data in an automated way. [ABSTRACT FROM AUTHOR]
Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: AutomaticAI – A hybrid approach for automatic artificial intelligence algorithm selection and hyperparameter tuning.
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  Data: <searchLink fieldCode="AR" term="%22Czako%2C+Zoltan%22">Czako, Zoltan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zoltan.czako@cs.utcluj.ro</i><br /><searchLink fieldCode="AR" term="%22Sebestyen%2C+Gheorghe%22">Sebestyen, Gheorghe</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gheorghe.sebestyen@cs.utcluj.ro</i><br /><searchLink fieldCode="AR" term="%22Hangan%2C+Anca%22">Hangan, Anca</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> anca.hangan@cs.utcluj.ro</i>
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Simulated+annealing%22">Simulated annealing</searchLink>
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  Data: • Automatic Artificial Intelligence Algorithm Selection. • Automatic Hyperparameter Optimization. • Particle Swarm Optimization which can handle continuous and discrete values. • Particle Swarm Optimization with Simulated Annealing acceptance criteria. • Global Optimal Solution. Recently, more and more real life problems are solved using artificial intelligence (AI) algorithms. One of the biggest challenges when working with AI is the selection and tuning of the best algorithm for solving the problem. The results generated by a given AI algorithm heavily depend on the way in which its hyperparameters are set. In most cases the process of algorithm selection and tuning is a manual, time consuming process in which the developer, based on experience and intuition tries to find the best solution from quality and execution time perspective. In this paper we present a method for solving the problem of AI algorithm selection and tuning, without human intervention, in a fully automated way. The method is a hybrid approach, a combination between particle swarm optimization and simulated annealing. We compare our approach with other similar tools like Auto-sklearn or Hyperopt-sklearn. We demonstrate the time efficiency and high accuracy of this method with some experiments on some known datasets. The paper also presents a platform for AI processing that include a set of procedures and services necessary in case of automatic processing of big datasets as well as the method for AI algorithm selection and tuning. This platform is useful for researchers and developers in an incipient phase of application development, when the best solution must be decided; it is also useful for specialists in different domains (physics, industry, economy) with less experience in using AI algorithms, but which has to process huge amount of data in an automated way. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=152077005
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.eswa.2021.115225
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      – Code: eng
        Text: English
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      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Algorithms
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      – SubjectFull: Particle swarm optimization
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      – SubjectFull: Problem solving
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      – SubjectFull: Simulated annealing
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      – TitleFull: AutomaticAI – A hybrid approach for automatic artificial intelligence algorithm selection and hyperparameter tuning.
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              Text: Nov2021
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              Y: 2021
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