Handling of Missing Values in FCM Clustering-based ANFIS with Partial Distance Strategy
Handling of Missing Values in FCM Clustering-based ANFIS with Partial Distance Strategy
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DOI：
10.1109/scisisis55246.2022.10001984
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Katsuhiro Honda;Satoshi Hyakutake;S. Ubukata;A. Notsu
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文献类型：
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作者：
Katsuhiro Honda;Satoshi Hyakutake;S. Ubukata;A. Notsu
自适应网络模糊推理系统（Adaptive Network Based Fuzzy Inference System，ANFIS）是一种很有前途的可解释神经网络模型，它基于神经元学习构造Takagi-Sugeno模糊推理系统，但不能很好地处理包含缺失值的不完整数据集。在这项研究中，提出了一种新的方法，使ANFIS对缺失值的鲁棒性，通过修改的前提部分模糊c均值（FCM）利用部分距离策略。通过使用部分距离的FCM准则计算具有缺失值的不完整对象的前提模糊隶属度，所提出的ANFIS变体可以产生ANFIS输出，即使某些对象在其输入观测中包含缺失值。通过一个时间序列数据的数值实验，证明了所提出的模型的特点。
Adaptive Network-based Fuzzy Inference System (ANFIS) is a promising model of explainable neural networks, which constructs Takagi-Sugeno fuzzy inference system based on neuro learning, but cannot work well with incomplete datasets including missing values. In this research, a novel approach of making ANFIS robust against missing values is proposed by modifying the premise part with Fuzzy c-Means (FCM) utilizing the partial distance strategy. By calculating the premise fuzzy memberships of incomplete objects having missing values with FCM criterion using partial distances, the proposed ANFIS variant can produce ANFIS outputs even if some objects include missing values in their input observation. The characteristics of the proposed model are demonstrated through a numerical experiment with a time series data.