- author = "Hengzhe Zhang and Aimin Zhou and Hong Qian and Hu Zhang",
- title = "{PS-Tree}: A piecewise symbolic regression tree",
- journal = "Swarm and Evolutionary Computation",
- year = "2022",
- volume = "71",
- pages = "101061",
- month = jun,
- keywords = "genetic algorithms, genetic programming, Regression tree, Symbolic regression, Multi-objective optimization, Evolutionary algorithm",
- ISSN = "2210-6502",
-
URL = "
https://www.sciencedirect.com/science/article/pii/S2210650222000335",
-
DOI = "
doi:10.1016/j.swevo.2022.101061",
- 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.",
-
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",
