Evolving simple and accurate symbolic regression models via asynchronous parallel computing

Sambo, Aliyu Sani; Azad, R. Muhammad Atif; Kovalchuk, Yevgeniya; Indramohan, Vivek; Shah, Hanifa

doi:10.1016/j.asoc.2021.107198

Cited by 12 publications

(3 citation statements)

References 22 publications

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“…Future research can focus on finding the optimal balance in determining the number of neurons/codebooks. Genetic programming (GP) [9] autonomously generates models and searches for solutions, maintaining accuracy with increasing sizes. In this paper, the authors suggest a novel approach Asynchronous Parsimony Pressure Genetic Programming (APGP).…”

Section: Review Of Literaturementioning

confidence: 99%

PreciSplit: A Novel Approach to Predicting Polynomial Regression Data

Verma,

Shah,

Shah

et al. 2024

Lecture Notes in Networks and Systems

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Time complexity is crucial in machine learning models, especially with large datasets, as it impacts efficiency and scalability. Reducing time complexity even by a fraction is necessary to avoid computational bottlenecks and enable faster model development cycles. It saves on resources, allowing for cost-effective operations. Data splitting involves dividing datasets into subsets for training, validation, and testing. It enables unbiased evaluation, prevents overfitting, and facilitates hyperparameter tuning and model selection. By evaluating models on separate test sets, their performance on unseen data can be assessed, leading to better generalization and more reliable machine learning systems. This paper introduces PreciSplit, a highly effective approach for data splitting based on the dataset's points of inflection. By strategically dividing the data at non-linear regions, PreciSplit creates nearly linear data segments, enabling the application of precise linear regression on each split region. Additionally, the evaluation of models on distinct test sets allows for comprehensive assessment of their performance on unseen data. This evaluation process enhances generalization capabilities and fosters the devel-opment of more reliable machine learning systems. The proposed PreciSplit method showcases promising potential for advancing data analysis and model accuracy. For smaller datasets, PreciSplit shows slower training times and lower accuracy compared to Support Vector Regression (SVR).However, as the dataset size grows,it exhibits a substantial improvement in training speed, outperforming SVR, while maintaining only a slight increase in error margin.

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Section: Review Of Literaturementioning

confidence: 99%

PreciSplit: A Novel Approach to Predicting Polynomial Regression Data

Verma,

Shah,

Shah

et al. 2024

Lecture Notes in Networks and Systems

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“…There are also cases where SR has been bound to reinforcement learning, and has been able to deal with dynamic tasks, with back-propagation capability [ 194 ] or even a dynamic process formulation [ 195 ]. Finally, since GP problems oftentimes require tons of computational time to complete, the evaluation time has been used as an estimate of model complexity and a new method is proposed to control it [ 196 ].…”

Section: Application In Science and Technologymentioning

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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“…In this section, the complexity of the proposed algorithm is analyzed in detail. Specifically, the study measures model complexity as the data complexity, model framework, the number of parameters and computational time (Sambo et al, 2021;Hu et al, 2021;Angerbauer et al, 2021).…”

Section: Model Complexitymentioning

confidence: 99%

User value identification based on an improved consumer value segmentation algorithm

Jin

et al. 2022

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PurposeThe purpose of this study is to propose a new consumer value segmentation method for low-dimensional dense market datasets to quickly detect and cluster the most profitable customers for the enterprises.Design/methodology/approachIn this study, the comprehensive segmentation bases (CSB) with richer meanings were obtained by introducing the weighted recency-frequency-monetary (RFM) model into the common segmentation bases (SB). Further, a new market segmentation method, the CSB-MBK algorithm was proposed by integrating the CSB model and the mini-batch k-means (MBK) clustering algorithm.FindingsThe results show that our proposed CSB model can reflect consumers' contributions to a market, as well as improve the clustering performance. Moreover, the proposed CSB-MBK algorithm is demonstrably superior to the SB-MBK, CSB-KMA and CSB-Chameleon algorithms with respect to the Silhouette Coefficient (SC), the Calinski-Harabasz (CH) Index , the average running time and superior to the SB-MBK, RFM-MBK and WRFM-MBK algorithms in terms of the inter-market value and characteristic differentiation.Practical implicationsThis paper provides a tool for decision-makers and marketers to segment a market quickly, which can help them grasp consumers' activity, loyalty, purchasing power and other characteristics in a target market timely and achieve the precision marketing.Originality/valueThis study is the first to introduce the CSB-MBK algorithm for identifying valuable customers through the comprehensive consideration of the clustering quality, consumer value and segmentation speed. Moreover, the CSB-MBK algorithm can be considered for applications in other markets.

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Evolving simple and accurate symbolic regression models via asynchronous parallel computing

Cited by 12 publications

References 22 publications

PreciSplit: A Novel Approach to Predicting Polynomial Regression Data

PreciSplit: A Novel Approach to Predicting Polynomial Regression Data

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

User value identification based on an improved consumer value segmentation algorithm

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