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Adaptive solution prediction for combinatorial optimization

Shen, Yunzhuang; Sun, Yuan; Li, Xiaodong; Eberhard, Andrew - In: European journal of operational research : EJOR 309 (2023) 3, pp. 1392-1408

Persistent link: https://www.econbiz.de/10014435020

Simultaneous sensitivity analysis of mixed-integer location-allocation models using machine learning tools : cancer hospitals' network design

Kordi, Afshin; Nemati, Arash - In: Operational research : an international journal 24 (2024) 2, pp. 1-32

Persistent link: https://www.econbiz.de/10015135104

Will artificial intelligence replace computational economists any time soon?

Maliar, Lilia; Maliar, Serguei; Winant, Pablo - 2019

Persistent link: https://www.econbiz.de/10012196835

Machine learning meets mathematical optimization to predict the optimal production of offshore wind parks

Fischetti, Martina; Fraccaro, Marco - In: Computers & operations research : and their … 106 (2019), pp. 289-297

Persistent link: https://www.econbiz.de/10012014562

Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm

Samadianfard, Saeed; Hashemi, Sajjad; Kargar, Katayoun; … - In: Energy reports 6 (2020), pp. 1147-1159

control renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. In the current study … Deylaman) to increase the accuracy of the subsequent hybrid model. The capability of the hybrid model in wind speed forecasting … indicated that the examined MLP-GA did not have a significant effect in increasing the estimation accuracy of standalone MLP …

Persistent link: https://www.econbiz.de/10012221074

Application of predictive methods to financial data sets

Habibi, Reza - In: Financial internet quarterly 17 (2021) 1, pp. 50-61

Financial data sets are growing too fast and need to be analyzed. Data science has many different techniques to store and summarize, mining, running simulations and finally analyzing them. Among data science methods, predictive methods play a critical role in analyzing financial data sets. In...

Persistent link: https://www.econbiz.de/10012588934

Markowitz mean-variance portfolio optimization with predictive stock selection using machine learning

Apichat Chaweewanchon; Rujira Chaysiri - In: International Journal of Financial Studies : open … 10 (2022) 3, pp. 1-19

With the advances in time-series prediction, several recent developments in machine learning have shown that integrating prediction methods into portfolio selection is a great opportunity. In this paper, we propose a novel approach to portfolio formation strategy based on a hybrid machine...

Persistent link: https://www.econbiz.de/10013368389

Optimal and robust combination of forecasts via constrained optimization and shrinkage

Roccazzella, Francesco; Gambetti, Paolo; Vrins, Frédéric - In: International journal of forecasting 38 (2022) 1, pp. 97-116

Persistent link: https://www.econbiz.de/10013347741

A modified Bayesian boosting algorithm with weight-guided optimal feature selection for sentiment analysis

Nayak, Smitha; Savita; Sharma, Yogesh Kumar - In: Decision analytics journal 8 (2023), pp. 1-13

. The goal is to improve the accuracy and efficiency of sentiment classification by selecting the most informative features … Boosting algorithm with weight-guided optimal feature selection achieves superior accuracy, precision, recall, and F1 score … classification performance. The results demonstrated that the proposed model had gained an accuracy level of 98.49%, even the Random …

Persistent link: https://www.econbiz.de/10014517151

Predict stock prices using supervised learning algorithms and particle swarm optimization algorithm

Bazrkar, Mohammad Javad; Hosseini, Soodeh - In: Computational economics 62 (2023) 1, pp. 165-186

Persistent link: https://www.econbiz.de/10014327292