Identifying Key Indicators for Successful Foreign Direct Investment through Asymmetric Optimization Using Machine Learning DOI Open Access

Aleksandar Kemiveš,

Milan Ranđelović,

Lidija Barjaktarović

et al.

Symmetry, Journal Year: 2024, Volume and Issue: 16(10), P. 1346 - 1346

Published: Oct. 11, 2024

The advancement of technology has led humanity into the era information society, where drives progress and knowledge is most valuable resource. This involves vast amounts data, from which stored should be effectively extracted for use. In this context, machine learning a growing trend used to address various challenges across different fields human activity. paper proposes an ensemble model that leverages multiple algorithms determine key factors successful foreign direct investment, simultaneously enables prediction process using data World Bank, covering 60 countries. innovative model, adds scientific research knowledge, employs two sets methods—binary regression feature selection—combined in stacking classification algorithm as combiner enable asymmetric optimization. proposed predictive been tested case study dataset compiled Bank countries worldwide. demonstrates better performance than each individual integrated it, are considered state-of-the-art these methodologies. Additionally, findings highlight three investment dataset, leading development optimized formula.

Language: Английский

Identifying Key Indicators for Successful Foreign Direct Investment through Asymmetric Optimization Using Machine Learning DOI Open Access

Aleksandar Kemiveš,

Milan Ranđelović,

Lidija Barjaktarović

et al.

Symmetry, Journal Year: 2024, Volume and Issue: 16(10), P. 1346 - 1346

Published: Oct. 11, 2024

The advancement of technology has led humanity into the era information society, where drives progress and knowledge is most valuable resource. This involves vast amounts data, from which stored should be effectively extracted for use. In this context, machine learning a growing trend used to address various challenges across different fields human activity. paper proposes an ensemble model that leverages multiple algorithms determine key factors successful foreign direct investment, simultaneously enables prediction process using data World Bank, covering 60 countries. innovative model, adds scientific research knowledge, employs two sets methods—binary regression feature selection—combined in stacking classification algorithm as combiner enable asymmetric optimization. proposed predictive been tested case study dataset compiled Bank countries worldwide. demonstrates better performance than each individual integrated it, are considered state-of-the-art these methodologies. Additionally, findings highlight three investment dataset, leading development optimized formula.

Language: Английский

Citations

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