最大边界模糊核超球分类方法

Classification method based on large margin and fuzzy kernel hyper-ball

为了提高多类问题的分类精度,提出最大边界模糊核超球(LMFKHB)算法。首先将样本数据通过核函数映射到高维数据特征空间,然后利用提出的方法找出各个判决函数;同时引入模糊隶属函数解决死区样本的错分问题,从而增强了算法适应性,提高了分类精度。人造数据和现实数据的实验结果表明最大边界模糊核超球算法具有较好的性能。

In order to improve the classification accuracy of muhiclass, an algorithm called Large Margin and Fuzzy Kernel Hyper-Ball （LMFKHB） was proposed. First, the sample datasets were mapped into a high-dimensional feature space through a kernel function. Then, all decision functions were obtained using the proposed method. Meanwhile, a fuzzy membership function was introduced to solve the wrong classification issue for these samples in the dead zone, thus the flexibility was enhanced and the classification accuracy was improved. The experiments on the artificial and real data demonstrate the effectiveness of the method.