期刊
APPLIED SOFT COMPUTING
卷 134, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.asoc.2022.109958
关键词
Fuzzy rule-based model; Fuzzy clustering; Sampling; Dimensionality reduction; Granular computing; Information granularity
In this study, we propose a novel approach for selective sampling and mapping data reduction to address the challenges in dealing with multivariable and large-scale data. The approach focuses on reducing data variables and samples while preserving the structural characteristics of the original data. A multivariable data-driven fuzzy rule-based model is then developed based on the processed data. Experimental studies demonstrate that the proposed method outperforms existing regression algorithms in terms of effectiveness and efficiency.
Fuzzy rule-based models have been widely used due to their interpretability and effectiveness. However, they still encounter challenges when dealing with multivariable and large-scale data. In this study, we first propose a novel approach to establish a selective sampling and mapping data reduction method. The method focuses on reducing data variables while decreasing the number of samples, and an appropriate scaling size can be chosen for different situations. Then, a multivariable data-driven fuzzy rule-based model is developed based on the processed data. Moreover, the data projection approach using the distance metric helps to preserve the structural characteristics of the original data. The results are visually presented to facilitate an interpretable description of the subsequent rule-based modeling. Furthermore, due to the inevitable inaccuracy in the projection process of numeric modeling, we introduce the allocation of information granularity to extend the model to a granular form at a more abstract level. Experimental studies on both synthetic and publicly available datasets demonstrate that the proposed method has superior effectiveness and efficiency compared to the existing state-of-the-art regression algorithms.(c) 2022 Published by Elsevier B.V.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据