Predicting the Mutual Impact of Indicators of Legatum Prosperity Index using Bagging and Boosting Algorithms

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Naghash Asadi Ali; Mastourinejad Amin; Aghili Romina

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Journal Paper

Paper Information

Journal: JOURNAL OF ALGORITHMS AND COMPUTATION Year:2025 | Volume:57 | Issue:1 Page(s): 1-14

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Information Journal Paper

Title

Predicting the Mutual Impact of Indicators of Legatum Prosperity Index using Bagging and Boosting Algorithms

Author(s)

Naghash Asadi Ali | Mastourinejad Amin | Aghili Romina | Issue Writer Certificate

Keywords

Data Mining

Machine learning

Prediction models

Bagging and Boosting algorithms

Legatum prosperity index

Abstract

Decision-making is a critical aspect of governance, and Data Mining and Machine learning algorithms can significantly enhance this process. By leveraging predictive models generated through these algorithms, policymakers can make more informed and accurate decisions. Since such models rely on historical data, the Legatum prosperity index serves as a valuable source for this analysis. In this study, Bagging and Boosting algorithms are employed to develop predictive models and analyze various indicators across different continents. The results demonstrate the effectiveness of these algorithms in forecasting key indicators and reveal notable differences in the influential factors across regions. These findings can support policymakers in formulating targeted strategies to enhance governance and living standards, considering regional characteristics and priorities. Furthermore, providing predictive models for each indicator allows countries to forecast and assess the impact of improving a specific indicator on others.

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