基于局部密度比的模糊隶属度设置算法

基于知识发现中的局部异常因子思想, 提出一种基于局部密度比的模糊隶属度设置算法, 该算法根据样本的邻域密度比设置样本的隶属度, 并采用一种单参数选择策略. 数值实验表明, 所提出的算法在带噪声的非线性函数估计方面具有很好的鲁棒性, 有效地解决了模糊支持向量机中的模糊隶属度设置问题, 对处理带噪声的分类和非线性函数估计问题具有重要的意义.

A Local density ratio Based Algorithm for Setting Fuzzy Memberships

Based on the local outlier factor (LOF) for detecting outlier in knowledge discovery, a local density ratio (LDR) based setting fuzzy membership algorithm was developed. In the proposed algorithm, the fuzzy memberships are assigned to the samples according to their neighborhood density ratios and a single parameter selection strategy is also adopted. Numerical experiments showed that the proposed algorithm possesses a good robustness for nonlinear function estimation problems with noise data. The presented algorithm is effective for setting fuzzy memberships in fuzzy support vector machine, which is important to deal with classification problems and nonlinear function estimation problems with noise data.