Hyperparameter optimization based on a priori and a posteriori knowledge about classification problem
Subject of Research. The paper deals with Bayesian method for hyperparameter optimization of algorithms, used in machine learning for classification problems. A comprehensive survey is carried out about using a priori and a posteriori knowledge in classification task for hyperparameter optimization...
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| Vydané v: | Nauchno-tekhnicheskiĭ vestnik informat͡s︡ionnykh tekhnologiĭ, mekhaniki i optiki Ročník 20; číslo 6; s. 828 - 834 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
ITMO University
01.12.2020
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| Predmet: | |
| ISSN: | 2226-1494, 2500-0373 |
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| Abstract | Subject of Research. The paper deals with Bayesian method for hyperparameter optimization of algorithms, used in machine learning for classification problems. A comprehensive survey is carried out about using a priori and a posteriori knowledge in classification task for hyperparameter optimization quality improvement. Method. The existing Bayesian optimization algorithm for hyperparameter setting in classification problems was expanded. We proposed a target function modification calculated on the basis of hyperparameters optimized for the similar problems and a metric for determination of similarity classification problems based on generated meta-features. Main Results. Experiments carried out on the real-world datasets from OpenML database have confirmed that the proposed algorithm achieves usually significantly better performance results than the existing Bayesian optimization algorithm within a fixed time limit. Practical Relevance. The proposed algorithm can be used for hyperparameter optimization in any classification problem, for example, in medicine, image processing or chemistry. |
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| AbstractList | Subject of Research. The paper deals with Bayesian method for hyperparameter optimization of algorithms, used in machine learning for classification problems. A comprehensive survey is carried out about using a priori and a posteriori knowledge in classification task for hyperparameter optimization quality improvement. Method. The existing Bayesian optimization algorithm for hyperparameter setting in classification problems was expanded. We proposed a target function modification calculated on the basis of hyperparameters optimized for the similar problems and a metric for determination of similarity classification problems based on generated meta-features. Main Results. Experiments carried out on the real-world datasets from OpenML database have confirmed that the proposed algorithm achieves usually significantly better performance results than the existing Bayesian optimization algorithm within a fixed time limit. Practical Relevance. The proposed algorithm can be used for hyperparameter optimization in any classification problem, for example, in medicine, image processing or chemistry. |
| Author | Efimova, V.A. Shalamov, V.V. Filchenkov, A.A. Smirnova, V.S. |
| Author_xml | – sequence: 1 givenname: V.S. surname: Smirnova fullname: Smirnova, V.S. – sequence: 2 givenname: V.V. surname: Shalamov fullname: Shalamov, V.V. – sequence: 3 givenname: V.A. surname: Efimova fullname: Efimova, V.A. – sequence: 4 givenname: A.A. surname: Filchenkov fullname: Filchenkov, A.A. |
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| Title | Hyperparameter optimization based on a priori and a posteriori knowledge about classification problem |
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