PS-Tree: A piecewise symbolic regression tree
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- @Article{Zhang:2022:swarmEC,
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author = "Hengzhe Zhang and Aimin Zhou and Hong Qian and
Hu Zhang",
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title = "{PS-Tree}: A piecewise symbolic regression tree",
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journal = "Swarm and Evolutionary Computation",
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year = "2022",
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volume = "71",
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pages = "101061",
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month = jun,
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keywords = "genetic algorithms, genetic programming, Regression
tree, Symbolic regression, Multi-objective
optimization, Evolutionary algorithm",
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ISSN = "2210-6502",
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URL = "https://www.sciencedirect.com/science/article/pii/S2210650222000335",
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DOI = "doi:10.1016/j.swevo.2022.101061",
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abstract = "The symbolic methods have recently regained popularity
due to their reasonable interpretability compared to
neural network-based artificial intelligence
techniques. The regression tree is such a symbolic
method that divides the feature space into several
subregions and builds a simple response surface model,
such as a constant value or a linear model, for each
subregion. However, this strategy may fail when
nonlinear structures exist in the subregions. To
overcome this problem, this paper proposes a new
regression model, named piecewise symbolic regression
tree (PS-Tree). Instead of using constant values or
linear models as the leaf nodes, PS-Tree builds
symbolic regressors for the leaf nodes or subregions.
In addition to that, we also propose an adaptive space
partition strategy by dynamically adjusting the
partition of the space to alleviate the problem caused
by incorrect partitioning. PS-Tree is applied to 122
synthetic and real-world datasets, and the results show
that it outperforms several state-of-the-art regression
methods.",
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notes = "Also known as \cite{ZHANG2022101061}
Shanghai Institute of AI for Education and School of
Computer Science and Technology, East China Normal
University, Shanghai 200062, China",
- }
Genetic Programming entries for
Hengzhe Zhang
Aimin Zhou
Hong Qian
Hu Zhang
Citations