基于熵和蚁群聚类算法的模糊支持向量机 |
| |
引用本文: | 王琳,闫德勤,梁宏霞. 基于熵和蚁群聚类算法的模糊支持向量机[J]. 计算机应用, 2009, 29(7): 1890-1893 |
| |
作者姓名: | 王琳 闫德勤 梁宏霞 |
| |
作者单位: | 辽宁师范大学 |
| |
基金项目: | 国家级基金;省部级基金;市级基金;校级基金 |
| |
摘 要: | 摘 要:模糊支持向量机(FSVM)对传统支持向量机(SVM)在对外围点和噪声数据敏感的缺陷做了重要改进。选取合适的聚类中心计算符合数据本身特征分布的隶属度,能使分类更加准确,提高测试精度。论文基于模糊支持向量机思想,提出一种新的模糊聚类模型—基于熵和蚁群聚类算法的模糊支持向量机(EAFSVM),为聚类中心和隶属度的计算提出了新方法。实验对比传统SVM和FSVM,结果表明EAFSVM测试精度较高,尤其对多类数据、大规模数据具有较好的分类能力。
|
关 键 词: | 模糊支持向量机 熵 蚁群算法 聚类 fuzzy support vector machine entropy ant colony optimization clustering |
收稿时间: | 2009-01-05 |
修稿时间: | 2009-03-02 |
Fuzzy SVM with entropy membership and ant colony optimization |
| |
Abstract: | Abstract:Compared with traditional support vector machine (SVM), Fuzzy support vector machine (FSVM) made a great improvement in sensitive to outliers or noises. By selecting a proper clustering center to calculate membership, FSVM could obtain higher precision optimization hyperplane. Based on FSVM, through improving clustering center and membership, we propose a new fuzzy clustering method—Fuzzy SVM with Entropy Membership and Ant Colony Optimization(EAFSVM). Experiments show EAFSVM has a better precision and classification performance, especially to multi-class and large scale dates. |
| |
Keywords: | |
|
| 点击此处可从《计算机应用》浏览原始摘要信息 |
|
点击此处可从《计算机应用》下载全文 |
|