ResearchBib Share Your Research, Maximize Your Social Impacts
Sign for Notice Everyday Sign up >> Login

HYPERPARAMETER OPTIMIZATION BASED ON A PRIORI AND A POSTERIORI KNOWLEDGE ABOUT CLASSIFICATION PROBLEM

Journal: Scientific and Technical Journal of Information Technologies, Mechanics and Optics (Vol.20, No. 6)

Publication Date:

Authors : ;

Page : 828-834

Keywords : machine learning; classification; hyperparameter optimization; Bayesian optimization; Gaussian processes;

Source : Downloadexternal Find it from : Google Scholarexternal

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.

Last modified: 2020-12-05 01:13:19