Chapter

Nature-Inspired Design of Hybrid Intelligent Systems

Volume 667 of the series Studies in Computational Intelligence pp 787-800

Date:

Hyper-Parameter Tuning for Support Vector Machines by Estimation of Distribution Algorithms

  • Luis Carlos PadiernaAffiliated withTecnológico Nacional de México, Instituto Tecnológico de León
  • , Martín CarpioAffiliated withTecnológico Nacional de México, Instituto Tecnológico de León Email author 
  • , Alfonso RojasAffiliated withTecnológico Nacional de México, Instituto Tecnológico de León
  • , Héctor PugaAffiliated withTecnológico Nacional de México, Instituto Tecnológico de León
  • , Rosario BaltazarAffiliated withTecnológico Nacional de México, Instituto Tecnológico de León
  • , Héctor FraireAffiliated withTecnológico Nacional de México, Instituto Tecnológico de Cd. Madero

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Abstract

Hyper-parameter tuning for support vector machines has been widely studied in the past decade. A variety of metaheuristics, such as Genetic Algorithms and Particle Swarm Optimization have been considered to accomplish this task. Notably, exhaustive strategies such as Grid Search or Random Search continue to be implemented for hyper-parameter tuning and have recently shown results comparable to sophisticated metaheuristics. The main reason for the success of exhaustive techniques is due to the fact that only two or three parameters need to be adjusted when working with support vector machines. In this chapter, we analyze two Estimation Distribution Algorithms, the Univariate Marginal Distribution Algorithm and the Boltzmann Univariate Marginal Distribution Algorithm, to verify if these algorithms preserve the effectiveness of Random Search and at the same time make more efficient the process of finding the optimal hyper-parameters without increasing the complexity of Random Search.

Keywords

Parameter tuning Support vector machines Hyper-parameters Estimation of distribution algorithms Pattern classification