Optimal Hyper-Parameter Tuning of SVM Classifiers With Application to Medical Diagnosis | Zendy

Alfonso Rojas-Dominguez | Zendy; Luis Carlos Padierna | Zendy; Juan Martin Carpio Valadez | Zendy; Hector J. Puga-Soberanes | Zendy; Hector J. Fraire | Zendy

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Optimal Hyper-Parameter Tuning of SVM Classifiers With Application to Medical Diagnosis

Author(s) -

Alfonso Rojas-Dominguez,

Luis Carlos Padierna,

Juan Martin Carpio Valadez,

Hector J. Puga-Soberanes,

Hector J. Fraire

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2779794

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Proper tuning of hyper-parameters is essential to the successful application of SVM-classifiers. Several methods have been used for this problem: grid search, random search, estimation of distribution Algorithms (EDAs), bio-inspired metaheuristics, among others. The objective of this paper is to determine the optimal method among those that recently reported good results: Bat algorithm, Firefly algorithm, Fruit-fly optimization algorithm, particle Swarm optimization, Univariate Marginal Distribution Algorithm (UMDA), and Boltzmann-UMDA. The criteria for optimality include measures of effectiveness, generalization, efficiency, and complexity. Experimental results on 15 medical diagnosis problems reveal that EDAs are the optimal strategy under such criteria. Finally, a novel performance index to guide the optimization process, that improves the generalization of the solutions while maintaining their effectiveness, is presented.

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