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首页> 外文期刊>IEEE Transactions on Fuzzy Systems >Data Preprocessing Technique for Neural Networks Based on Image Represented by a Fuzzy Function
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Data Preprocessing Technique for Neural Networks Based on Image Represented by a Fuzzy Function

机译：基于模糊函数表示的图像的神经网络数据预处理技术

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摘要

Although data preprocessing is a universal technique that can be widely used in neural networks (NNs), most research in this area is focused on designing new NN architectures. This paper, we propose a preprocessing technique that enriches the original image data using local intensity information; this technique is motivated by human perception. To encode this information into an image, we introduce a new image structure named image represented by a fuzzy function. When using this structure, a crisp intensity value of each pixel is replaced by a fuzzy set given by a membership function constructed with the usage of extremal values from the particular neighborhood of that pixel. We describe this structure and its properties and propose a way in which it can he used as an input into existing NNs without any modifications. Based on our benchmark consisting of three well-known datasets and five NN architectures, we show that the proposed preprocessing can, in mast cases, decrease classification error compared with a baseline and two other preprocessing methods. To support our claim, we have also selected several publicly available projects and tested the impact of the preprocessing with a positive result.
机译：虽然数据预处理是一种通用技术，可以广泛用于神经网络（NNS），但是该领域的大多数研究都集中在设计新的NN架构上。本文提出了一种预处理技术，可以使用本地强度信息丰富原始图像数据;这种技术受人类感知的动机。要将此信息进行编码到图像中，我们引入了一个由模糊函数表示的名为映像的新图像结构。当使用该结构时，每个像素的清晰强度值由由由来自该像素的特定邻域的使用极值的隶属函数给出的隶属函数给出的模糊集代替。我们描述了这种结构及其属性，并提出了一种方法，其中他可以用作现有NNS的输入而没有任何修改。基于我们的基准组成，由三个着名的数据集和五个NN架构组成，我们表明，与基线和另外两个预处理方法相比，所提出的预处理可以减少分类误差。为了支持我们的索赔，我们还选择了几个公开的项目，并测试了预处理的影响与积极的结果。

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