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
Enhancing the predictive performance of ensemble models through novel multi-objective strategies: evidence from credit risk and business model innovation survey data / Jha, Paritosh; Cucculelli, Marco. - In: ANNALS OF OPERATIONS RESEARCH. - ISSN 0254-5330. - STAMPA. - (2023), pp. 1-19. [10.1007/s10479-022-05028-0]
Enhancing the predictive performance of ensemble models through novel multi-objective strategies: evidence from credit risk and business model innovation survey data
Paritosh Jha
;cucculelli marco
2023-01-01
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 tasksI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.