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Reinforcement-Based Simultaneous Algorithm and Its Hyperparameters Selection

  • Valeria Efimova
  • Andrey FilchenkovEmail author
  • Anatoly Shalyto
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 794)

Abstract

There exist many algorithms for data analysis, especially for classification problems. To solve data analysis problem, a proper algorithm should be chosen, and also its hyperparameters should be selected. In this paper we present a new method for the simultaneous selection of an algorithm and its hyperparameters. In order to do so, we reduced this problem to the multi-armed bandit problem. We consider an algorithm as an arm and algorithm hyperparameters search during a fixed time as the corresponding arm play. We also suggest a problem-specific reward function. We performed the experiments on 10 real datasets and compare the suggested method with the existing one implemented in Auto-WEKA. The results show that our method is significantly better in most cases and never worse than the Auto-WEKA.

Keywords

Algorithm selection Hyperparameter optimization Multi-armed bandit Reinforcement learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Valeria Efimova
    • 1
  • Andrey Filchenkov
    • 1
    Email author
  • Anatoly Shalyto
    • 1
  1. 1.ITMO UniversitySt. PetersburgRussia

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