一种提高SVM分类能力的同步优化算法 A Synchronous Optimization Algorithm for Increasing Accuracy of SVM Classification

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择算法相比，具有输入特征少分类准确率高的优势。 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 optimization are two main factors that impact on classification accuracy. In order to improve the classification accuracy by optimizing parameter and choosing feature subset for SVM, a new algorithm is proposed through combining Bat Algorithm (BA) with SVM, termed BA + SVM. For assessing the performance 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.