PS-Tree: A piecewise symbolic regression tree

Zhang, Hengzhe; Zhou, Aimin; Qian, Hao; Zhang, Hu

doi:10.1016/j.swevo.2022.101061

Cited by 21 publications

(5 citation statements)

References 26 publications

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“…The concept of increased accuracy is the focal point of GP approaches as it constitutes a key element on the applicability of ML models [ 93 ]. There have been efforts on modifying the basic GP-SR procedure [ 18 , 94 , 95 , 96 , 97 , 98 ], while, on the other hand, some argue that GP-based procedures lead to abstract mathematical formulas that make no sense [ 83 ]. An intriguing idea that is also supported is the restriction of the search space into a set of symbols, by incorporating several constraints into the algorithm (usually by accepting prior knowledge about the system) [ 55 , 58 , 89 , 99 , 100 ].…”

Section: Symbolic Regressionmentioning

confidence: 99%

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

Angelis

Sofos

Karakasidis

2023

Arch Computat Methods Eng

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Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalizable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed.

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Section: Symbolic Regressionmentioning

confidence: 99%

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

Angelis

Sofos

Karakasidis

2023

Arch Computat Methods Eng

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“…More recently, there have been papers on modeling analysis based on regression trees approach in many fields. Such as the piecewise symbolic regression tree by Zhang et al [17], the boosted regression tree (Knierim et al [18], Said et al [19], Han et al [20], and Alnahit et al [21]), the logistic regression trees by Loh [22], the bayesian additive regression tree (Pan et al [23], Clark et al [24], Um et al [25]) and so on.…”

Section: Introductionmentioning

confidence: 99%

Partial Least Squares Regression Trees for Multivariate Response Data With Multicollinear Predictors

Yu,

Lee,

Cho

2024

IEEE Access

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Some problems arise in analyzing massive complex data consisting of multivariate response variables and a large number of multicollinear predictor variables, especially when the sample sizes compared to the number of predictors are small. Rather than ordinary linear regression modeling approaches, latent variable regression modeling approaches such as partial least squares regression can be used to capture the relationship between the response and predictor variables for such cases. However, for complex nonlinear relationships between the predictor and the response variable, the performance of inference and prediction using regression modeling approaches can be deflated. Regression trees can capture such complex relationships. Thus, we develop a partial least squares tree modeling algorithm that detects complex relationships and makes precise predictions by integrating the merits of partial least squares and regression trees. It is shown that it has better predictive performance than other methods through simulation and it is demonstrated that it generates interpretable predictive models with real data of usedcar and orthognathic surgrey.

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“…The analysis of market prices in the United States has been going on for decades, and a new method would be both welcome and useful. The regression trees discussed in H. Zhang et al (2022) are employed (regression trees have become more common in financial research. For example in Nakagawa and Sakemoto (2022)).…”

Section: Introductionmentioning

confidence: 99%

“…The regression trees discussed in H. Zhang et al. (2022) are employed (regression trees have become more common in financial research. For example in Nakagawa and Sakemoto (2022)).…”

Section: Introductionmentioning

confidence: 99%

CAPM and a new investment decision method

Taussig

2023

J Corp Accounting Finance

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The traditional CAPM explains the return on each security, while the current study suggests a new methodology for “picking” a broad index, based on regression trees. Analysts’ recommendations on market prices are analyzed, and a one to three‐stage method is employed. The analysis suggests a few rules of thumb (only one to three stages) for buying or selling the CRSP US market index. The findings are robust, and the rules are reliable. Researchers and practitioners may benefit greatly from the new rules.

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PS-Tree: A piecewise symbolic regression tree

Cited by 21 publications

References 26 publications

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

Partial Least Squares Regression Trees for Multivariate Response Data With Multicollinear Predictors

CAPM and a new investment decision method

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