CAJ | 학술논문

针对传统模糊支持向量机算法采用样本到类中心的距离关系来构建模糊隶属度函数存在不足，以及易受数据集不平衡的影响，提出了一种基于高斯分布的 FSVM，该方法既考虑了2类样本数量的不平衡问题，同时进一步考虑了样本不同方向上的分布特性。将样本的分布特性应用于模糊隶属度函数的设计，有效地提高了对正常样本和噪声、野值样本的区分能力。实验结果表明，在处理不平衡和有噪声干扰的数据集时，该方法较传统的 FSVM具有更强的鲁棒性。
침대전통모호지지향량궤산법채용양본도류중심적거리관계래구건모호대속도함수존재불족，이급역수수거집불평형적영향，제출료일충기우고사분포적 FSVM，해방법기고필료2류양본수량적불평형문제，동시진일보고필료양본불동방향상적분포특성。장양본적분포특성응용우모호대속도함수적설계，유효지제고료대정상양본화조성、야치양본적구분능력。실험결과표명，재처리불평형화유조성간우적수거집시，해방법교전통적 FSVM구유경강적로봉성。
Due to the defects in the fuzzy membership as a function of the distance of the sample points from the clus-ter center and its sensitivity to the imbalanced dataset in current fuzzy support vector machines (FSVM),a novel FS-VMbased on the Gaussian distribution is proposed.In the proposed method,the fuzzy membership is defined by not only the imbalance ratio between the numbers of two classes of datasets,but also those probability distributions.It can effectively improve the discrimination ability between normal samples with noise or outliers.Experimental results show that the proposed method is more robust than the traditional FSVMfor the imbalanced datasets with noise.