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...

Celý popis

Podrobná bibliografie

Vydáno v:	IEEE transactions on cybernetics Ročník 51; číslo 11; s. 5468 - 5482
Hlavní autoři:	Lensen, Andrew, Xue, Bing, Zhang, Mengjie
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.11.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	<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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!