Tax Share Analysis and Prediction of Kernel Extreme Learning Machine Optimized by Vector Weighted Average Algorithm

Research Article
Open access

Tax Share Analysis and Prediction of Kernel Extreme Learning Machine Optimized by Vector Weighted Average Algorithm

Ziqi Lin 1*
  • 1 Cornell University    
  • *corresponding author zl825@cornell.edu
Published on 20 June 2025 | https://doi.org/10.54254/2754-1169/2025.24147
AEMPS Vol.194
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-213-3
ISBN (Online): 978-1-80590-214-0

Abstract

In this paper, a kernel Extreme Learning Machine (KELM) model based on vector weighted average algorithm is proposed for the prediction of national tax revenue ratio, which provides a new way of thinking and method for tax revenue prediction. By analyzing the correlation between each index and tax share, it is found that gasoline price and life expectancy are significantly positively correlated with tax share, while fertility rate and birth rate are significantly negatively correlated. The model shows excellent predictive performance on both training set and test set, with an R² of 0.995 in training set and 0.994 in test set, indicating that the model has excellent generalization ability. In addition, the root mean square error (RMSE) of the training set and the test set are 0.185 and 0.177, respectively, and the relative prediction deviation (RPD) is 14.234 and 13.178, respectively, which further verifies the high accuracy and stability of the model. Scatter plots of actual predicted versus actual values show that the model is able to accurately capture trends in tax shares with little prediction error. In summary, the optimized KELM model proposed in this paper not only has excellent performance on known data, but also has good expansion ability, and can be effectively applied to the tax share prediction of unknown data, providing a reliable tool for relevant policy making and economic analysis. The research of this paper provides a new technical path for the field of tax forecasting, which has important theoretical significance and practical value.

Keywords:

Vector weighted average algorithm, Nuclear extreme learning machine, Tax share forecast

Lin,Z. (2025). Tax Share Analysis and Prediction of Kernel Extreme Learning Machine Optimized by Vector Weighted Average Algorithm. Advances in Economics, Management and Political Sciences,194,1-8.
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References

[1]. Köppl, Angela, and Margit Schratzenstaller. "Carbon taxation: A review of the empirical literature." Journal of Economic Surveys 37.4 (2023): 1353-1388.

[2]. Piketty, Thomas, Emmanuel Saez, and Gabriel Zucman. "Rethinking capital and wealth taxation." Oxford Review of Economic Policy 39.3 (2023): 575-591.

[3]. Jacob, Martin. "Real effects of corporate taxation: A review." European Accounting Review 31.1 (2022): 269-296.

[4]. Yixin Zhang and Yisong Chen. "The Role of Machine Learning in Reducing Healthcare Costs: The Impact of Medication Adherence and Preventive Care on Hospitalization Expenses." arXiv preprint arXiv:2504.07422 [cs.LG], 2025.

[5]. Guvenen, Fatih, et al. "Use it or lose it: Efficiency and redistributional effects of wealth taxation." The Quarterly Journal of Economics 138.2 (2023): 835-894.

[6]. Delgado, Francisco J., Jaume Freire-González, and Maria J. Presno. "Environmental taxation in the European Union: Are there common trends?." Economic Analysis and Policy 73 (2022): 670-682.

[7]. Avi-Yonah, Reuven, Young Ran Kim, and Karen Sam. "A new framework for digital taxation." Harv. Int'l LJ 63 (2022): 279.

[8]. World Health Organization. WHO manual on sugar-sweetened beverage taxation policies to promote healthy diets. World Health Organization, 2022..

[9]. Bachas, Pierre, et al. Globalization and factor income taxation. No. w29819. National Bureau of Economic Research, 2022.

[10]. Abigail, Ekezie Chineze. "Unlocking Economic Growth Through Taxation in the case Nigeria." Journal of Business and Economic Options 6.4 (2023): 21-27.

[11]. Andreyeva, Tatiana, et al. "Outcomes following taxation of sugar-sweetened beverages: a systematic review and meta-analysis." JAMA Network Open 5.6 (2022): e2215276-e2215276.

[12]. Ahmad, Munir, and Elma Satrovic. "How do transportation-based environmental taxation and globalization contribute to ecological sustainability?." Ecological Informatics 74 (2023): 102009.

[13]. Nchofoung, Tii N., Hervé Kaffo Fotio, and Clovis Wendji Miamo. "Green taxation and renewable energy technologies adoption: A global evidence." Renewable Energy Focus 44 (2023): 334-343.

[14]. Dray, Sacha, Camille Landais, and Stefanie Stantcheva. Wealth and property taxation in the United States. No. w31080. National Bureau of Economic Research, 2023.

[15]. Saez, Emmanuel, and Gabriel Zucman. "Wealth taxation: lessons from history and recent developments." AEA Papers and Proceedings. Vol. 112. 2014 Broadway, Suite 305, Nashville, TN 37203: American Economic Association, 2022.

[16]. Ganghof, Steffen. The politics of income taxation: A comparative analysis. ecpr Press, 2024.

[17]. Hebous, Shafik, and Michael Keen. "Pareto-improving minimum corporate taxation." Journal of Public Economics 225 (2023): 104952.

[18]. Paul, Asim, et al. "A green inventory model with the effect of carbon taxation." Annals of Operations Research 309.1 (2022): 233-248.

[19]. Paraje, Guillermo R., et al. "Taxation of tobacco, alcohol, and sugar-sweetened beverages: reviewing the evidence and dispelling the myths." BMJ Global Health 8.Suppl 8 (2023): e011866.

[20]. Vishnevsky, V. P., et al. "The principles of taxation for digital economy." Terra Economicus 20.2 (2022): 59-71.

[21]. Máté, Domicián, László Török, and Judit T. Kiss. "The impacts of energy supply and environmental taxation on carbon intensity." Technological and Economic Development of Economy 29.4 (2023): 1195-1215.

[22]. Yunchong Liu , Xiaorui Shen, Yeyubei Zhang, Zhongyan Wang, Yexin Tian, Jianglai Dai, and Yuchen Cao. "A Systematic Review of Machine Learning Approaches for Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases." arXiv preprint arXiv:2410.20293 [cs.LG], 2025

Cite this article

Lin,Z. (2025). Tax Share Analysis and Prediction of Kernel Extreme Learning Machine Optimized by Vector Weighted Average Algorithm. Advances in Economics, Management and Political Sciences,194,1-8.

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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume title: Proceedings of the 9th International Conference on Economic Management and Green Development

ISBN：978-1-80590-213-3(Print) / 978-1-80590-214-0(Online)
Editor：Florian Marcel Nuţă
Conference website: https://www.icemgd.org/
Conference date: 26 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.194
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Köppl, Angela, and Margit Schratzenstaller. "Carbon taxation: A review of the empirical literature." Journal of Economic Surveys 37.4 (2023): 1353-1388.

[2]. Piketty, Thomas, Emmanuel Saez, and Gabriel Zucman. "Rethinking capital and wealth taxation." Oxford Review of Economic Policy 39.3 (2023): 575-591.

[3]. Jacob, Martin. "Real effects of corporate taxation: A review." European Accounting Review 31.1 (2022): 269-296.

[4]. Yixin Zhang and Yisong Chen. "The Role of Machine Learning in Reducing Healthcare Costs: The Impact of Medication Adherence and Preventive Care on Hospitalization Expenses." arXiv preprint arXiv:2504.07422 [cs.LG], 2025.

[5]. Guvenen, Fatih, et al. "Use it or lose it: Efficiency and redistributional effects of wealth taxation." The Quarterly Journal of Economics 138.2 (2023): 835-894.

[6]. Delgado, Francisco J., Jaume Freire-González, and Maria J. Presno. "Environmental taxation in the European Union: Are there common trends?." Economic Analysis and Policy 73 (2022): 670-682.

[7]. Avi-Yonah, Reuven, Young Ran Kim, and Karen Sam. "A new framework for digital taxation." Harv. Int'l LJ 63 (2022): 279.

[8]. World Health Organization. WHO manual on sugar-sweetened beverage taxation policies to promote healthy diets. World Health Organization, 2022..

[9]. Bachas, Pierre, et al. Globalization and factor income taxation. No. w29819. National Bureau of Economic Research, 2022.

[10]. Abigail, Ekezie Chineze. "Unlocking Economic Growth Through Taxation in the case Nigeria." Journal of Business and Economic Options 6.4 (2023): 21-27.

[11]. Andreyeva, Tatiana, et al. "Outcomes following taxation of sugar-sweetened beverages: a systematic review and meta-analysis." JAMA Network Open 5.6 (2022): e2215276-e2215276.

[12]. Ahmad, Munir, and Elma Satrovic. "How do transportation-based environmental taxation and globalization contribute to ecological sustainability?." Ecological Informatics 74 (2023): 102009.

[13]. Nchofoung, Tii N., Hervé Kaffo Fotio, and Clovis Wendji Miamo. "Green taxation and renewable energy technologies adoption: A global evidence." Renewable Energy Focus 44 (2023): 334-343.

[14]. Dray, Sacha, Camille Landais, and Stefanie Stantcheva. Wealth and property taxation in the United States. No. w31080. National Bureau of Economic Research, 2023.

[15]. Saez, Emmanuel, and Gabriel Zucman. "Wealth taxation: lessons from history and recent developments." AEA Papers and Proceedings. Vol. 112. 2014 Broadway, Suite 305, Nashville, TN 37203: American Economic Association, 2022.

[16]. Ganghof, Steffen. The politics of income taxation: A comparative analysis. ecpr Press, 2024.

[17]. Hebous, Shafik, and Michael Keen. "Pareto-improving minimum corporate taxation." Journal of Public Economics 225 (2023): 104952.

[18]. Paul, Asim, et al. "A green inventory model with the effect of carbon taxation." Annals of Operations Research 309.1 (2022): 233-248.

[19]. Paraje, Guillermo R., et al. "Taxation of tobacco, alcohol, and sugar-sweetened beverages: reviewing the evidence and dispelling the myths." BMJ Global Health 8.Suppl 8 (2023): e011866.

[20]. Vishnevsky, V. P., et al. "The principles of taxation for digital economy." Terra Economicus 20.2 (2022): 59-71.

[21]. Máté, Domicián, László Török, and Judit T. Kiss. "The impacts of energy supply and environmental taxation on carbon intensity." Technological and Economic Development of Economy 29.4 (2023): 1195-1215.

[22]. Yunchong Liu , Xiaorui Shen, Yeyubei Zhang, Zhongyan Wang, Yexin Tian, Jianglai Dai, and Yuchen Cao. "A Systematic Review of Machine Learning Approaches for Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases." arXiv preprint arXiv:2410.20293 [cs.LG], 2025

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