| 摘要: |
| 深度随机配置网络(deep stochastic configuration network, DSCN)采取前馈学习方式, 基于特有的监督机制随机分配节点参数, 具有全局逼近性. 但是, 在实际场景下, 数据采集过程中潜在的离群值和噪声, 易对分类结果产生负面影响. 为提高DSCN解决二分类问题的性能, 基于DSCN引入直觉模糊数思想, 提出了一种直觉模糊深度随机配置网络(intuitionistic fuzzy deep stochastic configuration network, IFDSCN). 与标准DSCN不同, IFDSCN通过计算样本隶属度和非隶属度, 为每个样本分配一个直觉模糊数, 通过加权的方法来生成最优分类器, 以克服噪声和异常值对数据分类的负面影响. 在8个基准数据集上的实验结果表明, 所提出的模型与直觉模糊孪生支持向量机(intuitionistic fuzzy twin support vector machine, IFTWSVM)、核岭回归(kernel ridge regression, KRR)、直觉模糊核岭回归(intuitionistic fuzzy kernel ridge regression, IFKRR)、随机函数向量链接神经网络(random vector functional link neural network, RVFL)和SCN等学习模型相比, IFDSCN具有更好的二分类性能. |
| 关键词: 直觉模糊数 随机配置网络 二分类 数据噪声 神经网络 |
| DOI：10.13328/j.cnki.jos.007291 |
| 分类号:TP18 |
| 基金项目:国家自然科学基金(62276265, 61976216) |
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| Intuitionistic Fuzzy Deep Stochastic Configuration Network for Solving Binary Classification Problems |
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DING Shi-Fei1,2, ZHU Jiang-Lan1, ZHANG Cheng-Long1, GUO Li-Li1,2, ZHANG Jian1,2
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1.School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;2.Mine Digitization Engineering Research Center of Ministry of Education (China University of Mining and Technology), Xuzhou 221116, China
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| Abstract: |
| Deep stochastic configuration network (DSCN) adopts a feedforward learning approach and randomly assigns node parameters based on a unique supervisory mechanism, which has universal approximation. However, in actual scenarios, the potential outliers and noise during data collection can negatively affect the classification results. To improve the performance of DSCN in solving binary classification problems, this study introduces the idea of intuitionistic fuzzy numbers based on DSCN and proposes an intuitionistic fuzzy deep stochastic configuration network (IFDSCN). Different from the standard DSCN, IFDSCN assigns an intuitionistic fuzzy number to each sample by calculating the sample membership and non-membership, and generates the optimal classifier by a weighting method to overcome the negative effect of noise and outliers on data classification. The experimental results on eight benchmark datasets show that compared to other learning models including the intuitionistic fuzzy twin support vector machine (IFTWSVM), kernel ridge regression (KRR), intuitionistic fuzzy kernel ridge regression (IFKRR), random vector functional link neural network (RVFL), and SCN, IFDSCN has better binary classification performance. |
| Key words: intuitionistic fuzzy number stochastic configuration network binary classification data outlier neural network |
