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Your search keyword '"Jahangoshai Rezaee, Mustafa"' showing total 146 results
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146 results on '"Jahangoshai Rezaee, Mustafa"'

13. An explainable data-driven decision support framework for strategic customer development

Author

Abbaspour Onari, Mohsen, Jahangoshai Rezaee, Mustafa, Saberi, Morteza, Nobile, Marco S., Abbaspour Onari, Mohsen, Jahangoshai Rezaee, Mustafa, Saberi, Morteza, and Nobile, Marco S.

Abstract

Financial institutions benefit from the advanced predictive performance of machine learning algorithms in automatic decision-making for credit scoring. However, two main challenges hamper machine learning algorithms’ applicability in practice: the complex and black-box nature of algorithms that hinder their understandability and the inability to guide rejected customers to have a successful application. Regarding customer relationship management is one of the main responsibilities of financial institutions; they must clarify the decision-making process to guide them. However, financial institutions are not willing to disclose their decision-making procedure to prevent potential risks from customers or competitors side. Hence, in this study, a decision support framework is proposed to clarify the decision-making process and model strategic decision-making to guide rejected customers simultaneously. To do so, after classifying customers in their corresponding groups, the capability of Shapley additive exPlanations method is exploited to extract the most impactful features to the prediction's outcome globally and locally. Then, based on the benchmarking approach, the equivalent approved peer is found for the rejected customer for target setting to modify the application. To find the optimal modified values for a counterfactual prediction, a multi-objective gamed-based counterfactual explanation model is developed using the prisoner's dilemma game as the constraint to simulate strategic decision-making. After optimization, the decision is reported to the customers concerning the credential background. A public data set is used to elaborate on the proposed framework. This framework can generate counterfactual predictions successfully by modifying perspective features.

Published
2024

27. A Hybrid Semi-Supervised Approach for Estimating the Efficient and Optimal Level of Hospitals Outputs.

Author

Jozmaleki, Mehrdad, Jahangoshai Rezaee, Mustafa, and Saberi, Morteza

Abstract

Despite various supervised methods and algorithms, prediction methods do not necessarily provide the optimal values of the outputs. In this paper, an approach is proposed based on the integration of the clustering algorithm and a new mathematical programming model for predicting the efficient and optimal level of hospital outputs. In the first stage, units are evaluated using data envelopment analysis (DEA) and then, high-efficiency units are selected. In the second stage, the selected units are clustered based on the inputs of the units using the fuzzy C-means algorithm. For each unit to be estimated, the corresponding cluster is found and the closest unit to the target unit is determined. Finally, using the proposed new mathematical programming model, the optimal values are estimated based on the more efficient unit. A case study on Iranian hospitals illustrates the implementation of algorithms and methods and shows the abilities of the proposed approach. [ABSTRACT FROM AUTHOR]

Published
2024
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