Support Vector Machine Optimized by Fireworks Algorithm for Handwritten Digit Recognition

Eva Tuba, Romana Capor Hrosik, Adis Alihodzic, Raka Jovanovic, Milan Tuba

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

Abstract

Handwritten digit recognition is an important subarea in the object recognition research area. Support vector machines represent a very successful recent binary classifier. Basic support vector machines have to be improved in order to deal with real-world problems. The introduction of soft margin for outliers and misclassified samples as well as kernel function for non linearly separably data leads to the hard optimization problem of selecting parameters for these two modifications. Grid search which is often used is rather inefficient. In this paper we propose the use of one of the latest swarm intelligence algorithms, the fireworks algorithm, for the support vector machine parameters tuning. We tested our approach on standard MNIST base of handwritten images and with selected set of simple features we obtained better results compared to other approaches from literature.

Original languageEnglish
Title of host publicationModelling and Development of Intelligent Systems - 6th International Conference, MDIS 2019, Revised Selected Papers
EditorsDana Simian, Laura Florentina Stoica
PublisherSpringer
Pages187-199
Number of pages13
ISBN (Print)9783030392369
DOIs
Publication statusPublished - 1 Jan 2020
Event6th International Conference on Modelling and Development of Intelligent Systems, MDIS 2019 - Sibiu, Romania
Duration: 3 Oct 20195 Oct 2019

Publication series

NameCommunications in Computer and Information Science
Volume1126 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference6th International Conference on Modelling and Development of Intelligent Systems, MDIS 2019
CountryRomania
CitySibiu
Period3/10/195/10/19

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Keywords

  • Fireworks algorithm
  • Handwritten digit recognition
  • Machine learning
  • Optimization
  • Support vector machine
  • Swarm intelligence

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

Cite this

Tuba, E., Capor Hrosik, R., Alihodzic, A., Jovanovic, R., & Tuba, M. (2020). Support Vector Machine Optimized by Fireworks Algorithm for Handwritten Digit Recognition. In D. Simian, & L. F. Stoica (Eds.), Modelling and Development of Intelligent Systems - 6th International Conference, MDIS 2019, Revised Selected Papers (pp. 187-199). (Communications in Computer and Information Science; Vol. 1126 CCIS). Springer. https://doi.org/10.1007/978-3-030-39237-6_13