改进的模糊最小二乘支持向量机模型

Improved Fuzzy Least Squares Support Vector Machines Model

针对最小二乘支持向量机对噪声或孤立点敏感的问题,提出一种融合先验知识的模糊最小二乘支持向量机模型。在训练过程中考虑样本的噪声分布模型,结合样本紧密度策略,自动生成相应样本的模糊隶属度。实验结果表明,该模型对噪声样本具有较好的分类精度。

Aiming at the problem that the Least Squares Support Vector Machines（LSSVM） is sensitive to noises or outliers, a LSSVM model incorporating with a prior knowledge on data is proposed. Information of noise distribution for samples is introduced in the training process. A strategy based on the sample affinity is presented to discriminate data with noises. A fuzzy membership is automatically generated and assigned to each corresponding data point in the sample set by using the strategy and the noise model. Experimental result shows that the proposed model has better classification accuracy with noise data.