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Enhancing the predictive performance of ensemble models through novel multi-objective strategies: evidence from credit risk and business model innovation survey data

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  • Paritosh Jha

    (University of Bergamo)

  • Marco Cucculelli

    (Marche Polytechnic University)

Abstract

This paper proposes novel multi-objective optimization strategies to develop a weighted ensemble model. The comparison of the performance of the proposed strategies against simulated data suggests that the multi-objective strategy based on joint entropy is superior to other proposed strategies. For the application, generalization, and practical implications of the proposed approaches, we implemented the model on two real datasets related to the prediction of credit risk default and the adoption of the innovative business model by firms. The scope of this paper can be extended in ordering the solutions of the proposed multi-objective strategies and can be generalized for other similar predictive tasks.

Suggested Citation

  • Paritosh Jha & Marco Cucculelli, 2023. "Enhancing the predictive performance of ensemble models through novel multi-objective strategies: evidence from credit risk and business model innovation survey data," Annals of Operations Research, Springer, vol. 325(2), pages 1029-1047, June.
    • Handle: RePEc:spr:annopr:v:325:y:2023:i:2:d:10.1007_s10479-022-05028-0
    DOI: 10.1007/s10479-022-05028-0
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    References listed on IDEAS

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    1. Matthias Ehrgott, 2005. "Multicriteria Optimization," Springer Books, Springer, edition 0, number 978-3-540-27659-3, December.
    2. M. Dellnitz & O. Schütze & T. Hestermeyer, 2005. "Covering Pareto Sets by Multilevel Subdivision Techniques," Journal of Optimization Theory and Applications, Springer, vol. 124(1), pages 113-136, January.
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