College of Public Health, Affiliated Hospital of Hebei University, Hebei University, Baoding, 071000, Hebei, People's Republic of China.
Health Care Manag Sci. 2019 Sep;22(3):560-568. doi: 10.1007/s10729-019-09477-1. Epub 2019 Mar 8.
Nonlinear fuzzy classification models have better classification performance than linear fuzzy classifiers. In many nonlinear fuzzy classification problems, piecewise-linear fuzzy discriminant functions can approximate nonlinear fuzzy discriminant functions. In this paper, we first build fuzzy classifier based on data envelopment analysis (DEA) for incremental separable fuzzy training data, which can be widely applied in the healthcare management with fuzzy attributes, then we apply the proposed fuzzy DEA-based classifier in the diagnosis of Coronary with fuzzy symptoms and the classification of breast cancer dataset with fuzzy disturbance. Numerical experiments show the proposed fuzzy DEA-based classifier is accurate and robust.
非线性模糊分类模型比线性模糊分类器具有更好的分类性能。在许多非线性模糊分类问题中，分段线性模糊判别函数可以逼近非线性模糊判别函数。在本文中，我们首先为增量可分离模糊训练数据构建基于数据包络分析（DEA）的模糊分类器，该分类器可广泛应用于具有模糊属性的医疗保健管理中，然后将提出的基于模糊 DEA 的分类器应用于模糊症状的冠状动脉诊断和模糊干扰的乳腺癌数据集分类。数值实验表明，所提出的基于模糊 DEA 的分类器是准确和鲁棒的。
