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A convolutional neural network based classification for fuzzy datasets using 2-D transformation
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DOI：10.1016/j.asoc.2023.110732.png)
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Abstract
En 中文
Researches on deep learning methods have been actively conducted for the past 10 years, and various deep learning techniques have been proposed by many researchers. In addition, prediction methods using deep learning are widely used in various fields. In particular, convolution neural network (CNN) is most commonly applied to analyze visual images, but it can be also applied to many other data. On the other hand, fuzzy theory has been applied to deep learning techniques in traffic problem, agriculture, and airline customer service. In the case of data containing ambiguous information, data analysis can be performed using soft methods. In particular, the fuzzy theory is widely used to deal with such data. So, when the data includes vague information a fuzzy number can be applied to input/output data. In this paper, seven models using CNN have been proposed to analyze fuzzy input containing ambiguous or linguistic information. Our proposed models use five activation functions. For the data analysis, three datasets including Iris data, US Health Insurance data, Wine quality data are used to compare the seven proposed Fuzzy CNN models.& COPY; 2023 Elsevier B.V. All rights reserved.
Keywords:
Deep learning
Convolutional neural network
Fuzzy data
Iris dataset
US health insurance dataset
Journal
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