一种提高SVM分类能力的同步优化算法.pdf

Advances in Applied Mathematics 应用数学进展, 2017, 6(9), 1073-1081 Published Online December 2017 in Hans. /journal/aam /10.12677/aam.2017.69130 A Synchronous Optimization Algorithm for Increasing Accuracy of SVM Classification Fan He, Changjing Lu School of Mathematics and Physics, China University of Geosciences, Wuhan Hubei th th th Received: Nov. 24 , 2017; accepted: Dec. 7 , 2017; published: Dec. 14 , 2017 Abstract Support vector machines (SVM), which is a popular method for pattern classification, has been recently adopted in range of problems. In training procedure of SVM, feature selection and parameter optimi- zation are two main factors that impact on classification accuracy. In order to improve the classifica- tion accuracy by optimizing parameter and choosing feature subset for SVM, a new algorithm is pro- posed through combining Bat Algorithm (BA) with SVM, termed BA + SVM. For assessing the perfor- mance of BA + SVM, 10 public data-sets are employed to test the classification accuracy rate. Compared with grid algorithm, conventional parameter optimization method, our study concludes that BA + SVM has a higher classification accuracy with fewer input features for support vector classification. Keywords SVM, Bat Algorithm, Feature Selection, Parameter Optimization, Classification 一种提高SVM分类能力的同步优化算法 何 凡，卢常景 中国地质大学数学与物理学院，湖北 武汉 收稿日期：2017年11月24 日；录用日期：2017年12月7 日；发布日期：2017年12月14 日 摘 要 近年来，支持向量机(SVM)理论广泛应用于模式分类，然而影响其分类准确率的两个主要因素特征选择 和参数优化又是相互影响和制约的。文章提出一种BA + SVM算法，利用蝙蝠算法(BA)来同步完成SVM的 参数优化和输入数据的特征属性选择，提高了SVM的分类能力。设计的三种实验方式在10个测试数据集 文章引用: 何凡, 卢常景. 一种提高SVM 分类能力的同步优化算法[J]. 应用数学进展, 2017, 6(9): 1073-1081. DOI: 10.12677/aam.2017.69130 何凡，卢常景 上实验结果表明，BA + SVM 同步优化算法与单一进行参数优化或单一进行特征选