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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Vydáno v:Nauchno-tekhnicheskiĭ vestnik informat͡s︡ionnykh tekhnologiĭ, mekhaniki i optiki Ročník 20; číslo 6; s. 828 - 834
Hlavní autoři: Smirnova, V.S., Shalamov, V.V., Efimova, V.A., Filchenkov, A.A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: ITMO University 01.12.2020
Témata:
bayesian optimization
classification
gaussian processes
hyperparameter optimization
machine learning
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.
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.
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SubjectTerms bayesian optimization
classification
gaussian processes
hyperparameter optimization
machine learning
Title Hyperparameter optimization based on a priori and a posteriori knowledge about classification problem
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