论文标题
FCM-RDPA：使用模糊C均类聚类，正则化，下降和Powerball Anbelief的TSK模糊回归模型构建
FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief
论文作者
论文摘要
为了有效地优化用于回归问题的巨石 - 糖 - 康（TSK）模糊系统，最近提出了带有正则化，下降和Adabound（MBGD-RDA）算法的小批量梯度下降。本文进一步提出了FCM-RDPA，该论文通过Fuzzy c-Means聚集在规则初始化中替换了MBGD-RDA，并通过Powerball Adabelief进行了ADABOUND，该方法是在最近提议的Powerball Adabelief来进行的，该方法促进了拟议的PowerBall梯度和Adabelief，以进一步探索和稳定参数优化。在22个具有各种尺寸和维度的回归数据集上进行的广泛实验验证了FCM-RDPA优于MBGD-RDA的优势，尤其是当特征维度更高时。我们还提出了一种额外的方法，即FCM-RDPAX，该方法通过在规则的先例和结果中使用增强功能进一步改善FCM-RDPA。
To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. This paper further proposes FCM-RDpA, which improves MBGD-RDA by replacing the grid partition approach in rule initialization by fuzzy c-means clustering, and AdaBound by Powerball AdaBelief, which integrates recently proposed Powerball gradient and AdaBelief to further expedite and stabilize parameter optimization. Extensive experiments on 22 regression datasets with various sizes and dimensionalities validated the superiority of FCM-RDpA over MBGD-RDA, especially when the feature dimensionality is higher. We also propose an additional approach, FCM-RDpAx, that further improves FCM-RDpA by using augmented features in both the antecedents and consequents of the rules.
