基于超立方体顶点采样的区间数SVM分类模型研究

摘要

We study the support vector machine (SVM) classification problem of interval numbers,define the mapping relationship between interval numbers and the hypercube,and build a hypercube representation frame based on interval number samples.We then propose a super planar vertex sampling method based on the complete traversal of the binary tree,which can satisfy sample constraint.We also build up a classification-learning model through transforming the classification objective function.Experimental simulations verify the feasibility and effectiveness of the proposed method.%研究了针对区间数样本的支持向量机分类问题.定义了区间数样本与超立方体之间的映射关系,研究了基于区间数样本的超立方体表示框架;提出了基于二叉树完整遍历的满足样本约束的超平面顶点采样方法,建立了通过分类目标函数转换的分类学习模型.实验仿真结果表明了该方法的可行性与有效性.