Improved Fireworks Algorithm for Support Vector Machine Feature Selection and Parameters Optimization

SHEN Yong-liang; SONG Jie; WAN Zhi-chao

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Improved Fireworks Algorithm for Support Vector Machine Feature Selection and Parameters Optimization

更新时间：2026-09-04

- Improved Fireworks Algorithm for Support Vector Machine Feature Selection and Parameters Optimization
- Vol. 35, Issue 1, Pages: 21-25(2018)
- 作者机构：
  
  安徽大学计算机科学与技术学院,安徽,合肥,230601
- 作者简介：
- 基金信息：
- DOI：
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- Published：2018
- 稿件说明：
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沈永良, 宋杰, 万志超. 基于改进烟花算法的SVM特征选择和参数优化[J]. 微电子学与计算机, 2018, 35(1): 21-25.

SHEN Yong-liang, SONG Jie, WAN Zhi-chao. Improved Fireworks Algorithm for Support Vector Machine Feature Selection and Parameters Optimization[J]. 2018, 35(1): 21-25.
沈永良, 宋杰, 万志超. 基于改进烟花算法的SVM特征选择和参数优化[J]. 微电子学与计算机, 2018, 35(1): 21-25. DOI：

SHEN Yong-liang, SONG Jie, WAN Zhi-chao. Improved Fireworks Algorithm for Support Vector Machine Feature Selection and Parameters Optimization[J]. 2018, 35(1): 21-25. DOI：

摘要

本文提出了一种改进的基于烟花算法的SVM特征选择和参数优化算法.该算法针对特征选择问题的0-1特性

使用二进制编码的烟花算法

采用基于RBF核函数的SVM

在选取尽可能少的特征数目的同时提高了分类准确率.通过UCI数据仿真

对比结果表明: 该方法避免了过早成熟而陷入局部最优的问题

可有效地找出合适的特征子集及SVM参数

并取得较好的分类效果.

Abstract

In this paper

we propose a Fireworks Algorithm based method to improve the performance of feature selection and parameters optimization in training SVM. For the 0-1 characteristic of feature selection

the binary coding Fireworks Algorithm and RBF kernel function based SVM are used to improve the accuracy of classification with less features. Compared to previous works

the proposed method can avoid being mature and falling into a local value

and it can effectively find the appropriate feature subset and parameters to get better performance of classification in UCI dataset.

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references

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李小琳, 孙玥, 刘洋. 基于SVM修正的模糊时间序列模型在沪指预测中的应用[J]. 中国科学技术大学学报, 2016, 46(3): 238-246. DOI: 238

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