Genetic programming-based symbolic regression for goal-oriented dimension reduction
Created by W.Langdon from
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- @Article{DORGO:2021:CES,
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author = "Gyula Dorgo and Tibor Kulcsar and Janos Abonyi",
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title = "Genetic programming-based symbolic regression for
goal-oriented dimension reduction",
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journal = "Chemical Engineering Science",
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volume = "244",
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pages = "116769",
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year = "2021",
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ISSN = "0009-2509",
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DOI = "doi:10.1016/j.ces.2021.116769",
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URL = "https://www.sciencedirect.com/science/article/pii/S0009250921003341",
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keywords = "genetic algorithms, genetic programming, Data
visualisation, Software sensor, Online
near-infrared-spectroscopy, Classification, Principal
component analysis",
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abstract = "The majority of dimension reduction techniques are
built upon the optimization of an objective function
aiming to retain certain characteristics of the
projected datapoints: the variance of the original
dataset, the distance between the datapoints or their
neighbourhood characteristics, etc. Building upon the
optimization-based formalization of dimension reduction
techniques, the goal-oriented formulation of projection
cost functions is proposed. For the optimization of the
application-oriented data visualization cost function,
a Multi-gene genetic programming (GP)-based algorithm
is introduced to optimize the structures of the
equations used for mapping high-dimensional data into a
two-dimensional space and to select variables that are
needed to explore the internal structure of the data
for data-driven software sensor development or
classifier design. The main benefit of the approach is
that the evolved equations are interpretable and can be
used in surrogate models. The applicability of the
approach is demonstrated in the benchmark wine dataset
and in the estimation of the product quality in a
diesel oil blending technology based on an online
near-infrared (NIR) analyzer. The results illustrate
that the algorithm is capable to generate goal-oriented
and interpretable features, and the resultant simple
algebraic equations can be directly implemented into
applications when there is a need for computationally
cost-effective projections of high-dimensional data as
the resultant algebraic equations are computationally
simpler than other solutions as neural networks",
- }
Genetic Programming entries for
Gyula Dorgo
Tibor Kulcsar
Janos Abonyi
Citations