Bare Bones Fireworks Algorithm for Feature Selection and SVM Optimization

Eva Tuba, Ivana Strumberger, Nebojsa Bacanin, Raka Jovanovic, Milan Tuba

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Machine learning algorithms are used in various application and the need for faster and more accurate algorithms is urgent. Classification problem, as one of the most common machine learning tasks, has numerous proposed algorithms for solving it. One of the main factors that affects the classification accuracy, regardless of the used classifier, is the chosen feature set. Due to the fact that the classification quality depends on the features, feature selection represents an important task in machine learning. In this paper we propose adjusted bare bone fireworks algorithm for feature selection. Support vector machine is used as classifier, thus we additionally added SVM parameter optimization. The proposed method is tested on standard benchmark classification datasets from the UCI repository and the results are compared with other swarm intelligence methods. The results show that the proposed method achieves higher accuracy compared to the other methods even without SVM parameters tuning. As expected, parameter tuning has additionally increased the classification accuracy.

Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2207-2214
Number of pages8
ISBN (Electronic)9781728121536
DOIs
Publication statusPublished - Jun 2019
Event2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand
Duration: 10 Jun 201913 Jun 2019

Publication series

Name2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Conference

Conference2019 IEEE Congress on Evolutionary Computation, CEC 2019
CountryNew Zealand
CityWellington
Period10/6/1913/6/19

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Keywords

  • bare bones fireworks algorithm
  • classification
  • feature selection
  • k nearest neighbors
  • machine learning
  • optimization
  • support vector machine
  • swarm intelligence

ASJC Scopus subject areas

  • Computational Mathematics
  • Modelling and Simulation

Cite this

Tuba, E., Strumberger, I., Bacanin, N., Jovanovic, R., & Tuba, M. (2019). Bare Bones Fireworks Algorithm for Feature Selection and SVM Optimization. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 2207-2214). [8790033] (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2019.8790033