Open Access
ARTICLE
Improvement of the Firework Algorithm for Classification Problems
Yu Xue, Sow Alpha Amadou*, Yan Zhao
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
* Corresponding Author: Sow Alpha Amadou. Email:
Journal of Cyber Security 2020, 2(4), 191-196. https://doi.org/10.32604/jcs.2020.014045
Received 30 August 2020; Accepted 19 October 2020; Issue published 07 December 2020
Abstract
Attracted numerous analysts’ consideration, classification is one of the
primary issues in Machine learning. Numerous evolutionary algorithms (EAs)
were utilized to improve their global search ability. In the previous years, many
scientists have attempted to tackle this issue, yet regardless of the endeavors,
there are still a few inadequacies. Based on solving the classification problem,
this paper introduces a new optimization classification model, which can be
applied to the majority of evolutionary computing (EC) techniques. Firework
algorithm (FWA) is one of the EC methods, Although the Firework algorithm
(FWA) is a proficient algorithm for solving complex optimization issue. The
proficient of the FWA isn't fulfilled when being utilized for solving the
classification issues. In this paper we previously proposed optimization
classification model according to the classification issue. At that point we
legitimately utilize the model with FWA to solve the classification issue. Finally,
to investigate the performance of our model, we select 4 datasets in the
experiments, and the results indicate that an improved FWA can upgrade the
classification accuracy by using this model.
Keywords
Cite This Article
Y. Xue, S. Alpha Amadou and Y. Zhao, "Improvement of the firework algorithm for classification problems,"
Journal of Cyber Security, vol. 2, no.4, pp. 191–196, 2020. https://doi.org/10.32604/jcs.2020.014045