【摘要】 为了提高对存在噪声的大规模数据集的分类效果,本文提出了一种边界自适应三角模糊非线性优化支持向量分类器BAT-FNOSVC。该分类器在支持向量分类器SVC的基础上引入边界自适应三角模糊隶属函数以更好地解决噪声带来的干扰问题,同时在模型中构造模糊列核矩阵及稀疏化函数,提高了算法的可解释性。对含噪数据集的实验结果表明,与采用三角形模糊隶属函数的稀疏非线性优化分类器TFNOSVC、 SVC、 1-范数支持向量分类器L1SVC及最小二乘支持向量分类器LSSVC相比,BAT-FNOSVC的准确率有明显提高,说明BAT-FNOSVC算法对有噪声的数据集具有较好的分类效果。更多还原

【Abstract】 In order to improve the classification effect of large-scale noisy datasets, a boundary adaptive triangular fuzzy nonlinear optimization support vector classifier BAT-FNOSVC was proposed.Based on the support vector classifier SVC,the boundary adaptive triangular fuzzy membership function was introduced to better solve the interference problem caused by noise, and at the same time a fuzzy column kernel matrix and a sparse function were constructed in the model, which improves the interpretability of the algorithm.The experimental results on noisy data sets show that the accuracy of BAT-FNOSVC is significantly improved compared with triangular fuzzy nonlinear optimization support vector classifier TFNOSVC,SVC,1-norm support vector classifier L1 SVC and least squares support vector classifier LSSVC,indicating that the BAT-FNOSVC has the better classification effect on noisy datasets.更多还原