Data Preprocessing Technique for Neural Networks Based on Image Represented by a Fuzzy Function

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F20%3AA2101VJP" target="_blank" >RIV/61988987:17610/20:A2101VJP - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8691766" target="_blank" >https://ieeexplore.ieee.org/document/8691766</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TFUZZ.2019.2911494" target="_blank" >10.1109/TFUZZ.2019.2911494</a>

Result language
angličtina
Original language name
Data Preprocessing Technique for Neural Networks Based on Image Represented by a Fuzzy Function
Original language description
Although data preprocessing is a universal technique that can be widely used in neural networks, most research in this area is focused on designing new neural network architectures. In our work, 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 be used as an input into existing neural networks without any modifications. Based on our benchmark consisting of three well-known datasets and five neural network architectures, we show that the proposed preprocessing can, in most 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.
Czech name
—
Czech description
—

Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10102 - Applied mathematics

Project
<a href="/en/project/EF17_049%2F0008414" target="_blank" >EF17_049/0008414: Centre for the development of Artificial Intelligence Methods for the Automotive Industry of the region</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)