一种改进的偏二叉树孪生支持向量机算法及其应用

An Improved Partial Binary Tree Twin Support Vector Machine Algorithm and its Application

摘要:

针对二叉树分类性能受其层次结构影响较大的问题，提出了一种改进的偏二叉树孪生支持向量机多分类算法。该算法定义了一种基于加权样本类内距离和类间距离的混合分离性测度β，根据β的大小构造出合理层次结构的偏二叉树孪生支持向量机分类器。通过UCI数据集，验证了改进算法相较于其它SVM多分类算法的优越性。以风电机组齿轮箱故障诊断作为研究对象，基于时频联合方法提取其故障特征，应用改进的偏二叉树孪生支持向量机算法实现了对齿轮箱故障的准确识别。

Abstract:

Aiming at the problem that the classification performance of binary trees is greatly affected by its hierarchical structure,this paper proposes an improved multi-classification algorithm for partial binary tree twin support vector machine(TWSVM).The algorithm defines a mixed separation measure β,which based on sample class distance and interclass distance,and a suitable binary tree twin support vector machine classifier is constructed based on the value of β.Compared with other SVM multi-classification algorithm on UCI data sets,the superiority of improved algorithm is verified.Taking the wind turbine gearbox as the research object,feature vectors of faults were extracted based on time-frequency joint feature,and the faults were diagnosed by the improved partial binary tree TWSVM.