Genetic Programming for Evolving a Front of Interpretable Models for Data Visualization

Data visualization is a key tool in data mining for understanding big datasets. Many visualization methods have been proposed, including the well-regarded state-of-the-art method t-distributed stochastic neighbor embedding. However, the most powerful visualization methods have a significant limitati...

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Bibliographic Details

Published in:	IEEE transactions on cybernetics Vol. 51; no. 11; pp. 5468 - 5482
Main Authors:	Lensen, Andrew, Xue, Bing, Zhang, Mengjie
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.11.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">t -distributed stochastic neighborhood embedding Complexity theory Data mining Data visualization Datasets Feature selection (FS) Genetic algorithms Genetic programming genetic programming (GP) Manifolds Principal component analysis Scientific visualization Task analysis Visualization
ISSN:	2168-2267, 2168-2275, 2168-2275
Online Access:	Get full text
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