CAJ | 학술논문

针对支持向量机的多分类问题,提出一种新颖的基于非平行超平面的多分类簇支持向量机.它针对k模式分类问题分别训练产生k个分割超平面,每个超平面尽量靠近自身类模式而远离剩余类模式;决策时,新样本的类别由它距离最近的超平面所属的类决定,克服了一对一(OAO)和一对多(OAA)等传统方法存在的"决策盲区"和"类别不平衡"等缺陷.基于UCI和HCL2000数据集的实验表明,新方法在处理多分类问题时,识别精度显著优于传统多分类支持向量机方法.
침대지지향량궤적다분류문제,제출일충신영적기우비평행초평면적다분류족지지향량궤.타침대k모식분류문제분별훈련산생k개분할초평면,매개초평면진량고근자신류모식이원리잉여류모식;결책시,신양본적유별유타거리최근적초평면소속적류결정,극복료일대일(OAO)화일대다(OAA)등전통방법존재적"결책맹구"화"유별불평형"등결함.기우UCI화HCL2000수거집적실험표명,신방법재처리다분류문제시,식별정도현저우우전통다분류지지향량궤방법.
Based on the idea of nonparallel hyperplanes,a novel multi-class cluster support vector machine method was proposed to settle the multi-class classification problem of support vector machines. For a k-class classification problem,it trained k-hyperplanes respectively,and each one lay as close as possible to self-class while being apart from the rest classes as far as possible. Then,labels of new samples were determined by the class of their nearest hyperplane,thus the inherent limitations of One-Against-One (OAO) and One-Against-All (OAA) methods can be avoided,such as "decision blind-area" and "unbalanced classes". Finally,experiments on UCI and HCL2000 datasets show that the proposed method significantly outperforms traditional OAO and OAA in terms of recognition accuracy.