Improvement of the Firework Algorithm for Classification Problems

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.