摘要： 针对支持向量机多类分类问题，根据样本点集凸包找寻模式类间隙，通过提取模式类间隙多边形中轴线构造多类分类边界。当基本支持向量机扩展为多类分类问题时，该方法克服了OAO和OAA等传统方法存在的决策盲区和类别不平衡等缺陷。基于仿真数据集的 实验结果表明，构造的分类边界在保证分类精度的同时，能够使分类空隙最大化，实现对线性可分多类数据的最优分类。
Abstract: A new method to construct multi-class classifier based on linear SVM is proposed in the paper. Its major procedures include: to form interval space polygon among point sets by subtracting operation of convex hulls, to extract polygon axes and then extend to construct the classification boundaries. The method can avoid problems like blind area in decision-making and imbalance data sets like traditional multi-class classification ways of One-Against-All(OAA) and One-Agianst-One(OAO) encounter. Simulation test results show that classification boundaries constructed by the method can realize the minimum risk and the maximum interval space among point sets, thus can be seen as an embodiment of the optimal classification lines of multi-class point sets.
Support Vector Machine(SVM),
optimal classification line,