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Nature inspired optimization tools for SVMs - NIOTS.

Carlos Eduardo da Silva Santos1,2, Leandro Dos Santos Coelho1,3, Carlos Humberto Llanos1

  • 1Universidade de Brasília - UnB, Brasília - DF - Brasil.

Methodsx
|January 10, 2022
PubMed
Summary

This study introduces Nature Inspired Optimization Tools for Support Vector Machines (SVMs) to automate hyperparameter tuning. NIOTS addresses the Parameter Selection Problem by optimizing for both model precision and complexity.

Keywords:
Adaptive parameters controlDifferential evolution algorithmMulti-objective optimization problemParameters selection problemSupport vectors machines

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Area of Science:

  • Machine Learning
  • Computational Intelligence

Background:

  • Support Vector Machines (SVMs) are powerful tools for classification and regression.
  • Optimizing SVMs requires careful selection of hyperparameters, a challenge known as the Parameter Selection Problem (PSP).
  • Balancing model accuracy and complexity (number of support vectors) presents conflicting objectives.

Purpose of the Study:

  • To propose Nature Inspired Optimization Tools for SVMs (NIOTS) for automating the hyperparameter search process.
  • To model the PSP as a Multiobjective Optimization Problem (MOP) balancing precision and complexity.
  • To provide users with multiple optimal solutions for selecting the best SVM model.

Main Methods:

  • NIOTS automates the search for optimal SVM hyperparameters.
  • The Parameter Selection Problem is framed as a Multiobjective Optimization Problem with objectives for precision and low complexity.
  • Solutions are evaluated based on the trade-off between model precision and the number of support vectors.

Main Results:

  • NIOTS facilitates the automated identification of high-accuracy, low-complexity SVM models.
  • Users can explore solutions on the Pareto front to choose models balancing precision and complexity.
  • The framework supports various metaheuristics, including Adaptive Parameter with Mutant Tournament Multiobjective Differential Evolution (APMT-MODE), and multiple kernel options.

Conclusions:

  • NIOTS offers an effective method for automating SVM hyperparameter optimization.
  • The approach allows users to efficiently obtain and select suitable SVM models for their specific applications.
  • This automated process enhances the practical usability of SVMs by simplifying the complex parameter tuning.