Differential evolution-based parameters optimisation and feature selection for support vector machine

Jun Li; Lixin Ding; Bo Li

首页> 外文期刊>International Journal of Computational Science and Engineering >Differential evolution-based parameters optimisation and feature selection for support vector machine

【24h】

Differential evolution-based parameters optimisation and feature selection for support vector machine

机译：支持向量机的基于差分进化的参数优化和特征选择

获取原文

获取原文并翻译 | 示例

获取外文期刊封面封底 >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

This paper addresses the problem of SVM classification optimisation. For this purpose, the authors propose an SVM classification system based on differential evolution (DE) to improve the generalisation performance of the SVM classifier. In the classification system, a method of simultaneous parameters optimisation and feature selection for support vector machine is put forward. The experiments are conducted on the basis of benchmark dataset. The obtained results clearly confirm the superiority of the DE-SVM-FS approach compared to default SVM classifier and DE-SVM algorithm; this suggests that further substantial improvements in terms of classification accuracy can be achieved by the proposed DE-SVM-FS classification system.

机译：本文解决了支持向量机分类优化的问题。为此，作者提出了一种基于差分进化（DE）的SVM分类系统，以提高SVM分类器的泛化性能。在分类系统中，提出了一种支持向量机同时参数优化和特征选择的方法。实验是在基准数据集的基础上进行的。与默认的SVM分类器和DE-SVM算法相比，获得的结果清楚地证明了DE-SVM-FS方法的优越性。这表明通过提出的DE-SVM-FS分类系统可以实现分类准确度的进一步实质性提高。

著录项

来源
《International Journal of Computational Science and Engineering》 |2016年第4期|共9页
作者
Jun Li; Lixin Ding; Bo Li;
展开▼
作者单位

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
关键词
High-dimensional classification; Support vector machine; SVM; Differential evolution; Parameter optimisation; Feature selection;

机译：高维分类;支持向量机;支持向量机;差分演化;参数优化;特征选择;

相似文献

外文文献
中文文献
专利

1. Differential evolution-based parameters optimisation and feature selection for support vector machine [J] . Jun Li, Lixin Ding, Bo Li International Journal of Computational Science and Engineering . 2016,第4期

机译：支持向量机的基于差分进化的参数优化和特征选择
2. Artificial immune system based on adaptive clonal selection for feature selection and parameters optimisation of support vector machines [J] . Hashemipour Maryam Sadat, Soleimani Seyed Ali Connection Science . 2016,第1期

机译：基于自适应克隆选择的支持向量机特征选择和参数优化的人工免疫系统
3. Simultaneous feature selection and parameter optimisation of support vector machine using adaptive particle swarm gravitational search algorithm [J] . Malek Sarhani, Abdellatif El Afia International journal of metaheuristics . 2016,第1期

机译：自适应粒子群重力搜索算法的支持向量机同时特征选择和参数优化
4. Feature selection and support vector machine hyper-parameter optimisation for spam detection [C] . Melvin Diale, Christiaan Van Der Walt, Turgay Celik, International Conference on Pattern Recognition Association of South Africa and Robotics and Mechatronics . 2016

机译：用于垃圾邮件检测的特征选择和支持向量机超参数优化
5. Feature selection methods for support vector machines for two or more classes, with applications to the analysis of Alzheimer's disease and its onset with MRI brain image processing. [D] . Aksu, Yaman. 2010

机译：支持向量机的特征选择方法分为两类或更多类，可用于阿尔茨海默氏病的分析及其在MRI脑图像处理中的发作。
6. Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm [O] . Yong Mao, Xiao-bo Zhou, Dao-ying Pi, 2005

机译：高斯核支持向量机遗传算法在基因选择中的参数选择。
7. Autonomous Monitoring of Line-to-Line Faults in Photovoltaic Systems by Feature Selection and Parameter Optimization of Support Vector Machine Using Genetic Algorithms [O] . Aref Eskandari, Jafar Milimonfared, Mohammadreza Aghaei, 2020

机译：基于遗传算法的支持向量机的特征选择和参数优化自主监测光伏系统中的线到线路故障

Differential evolution-based parameters optimisation and feature selection for support vector machine

摘要

著录项

相似文献

相关主题

期刊订阅