The credit scoring is a statistical analysis performed by financial institutions to represent the creditworthiness of an individual or small and medium‐sized enterprise. A credit score is a numerical quantity that can be qualified by a rating label showing the potential risk. In order to determine which customers are likely to bring in the most revenue at the exact interest rate and credit limits, the lenders take into accounts these credit scores. In this context, the robust statistical models are inevitable to reduce the number of wrong decisions in the credit evaluation process. Although the machine learning approaches provide superior performance to conventional statistical methods, they are mostly criticized due to the selection of model structure, model complexity, tuning parameters, time consumption in the high‐dimensional and excessive nonlinear cases. For this reason, this study introduces an efficient model estimation and feature selection procedure for artificial neural network (ANN) classifiers in the context of credit scoring. Essentially, this procedure hybridizes training of ANNs with a novel feature selection approach based on genetic algorithms and information complexity criterion. In the application, the proposed procedure was performed on a couple of benchmark credit scoring datasets. According to analysis results, the proposed approach not only estimates robust models from ANNs in terms of model complexity, feature selection, and time consumption, but also outperforms the traditional training procedure for the classification accuracies, false positive, and false negative errors overtraining and test datasets.

Syrians have been obligated to leave their country and seek to asylum to the different countries mainly their neighbor countries; Turkey, Jordan and Lebanon. At the year of 2018, over 3,6 million Syrians live under temporary protection in Turkey In this study, perceptions towards Syrian refugees in Ankara were measured by means of the indexes of local people in Altındağ district where Syrians are populated to a high degree and Çankaya district where Syrian are populated to a low degree. Based on the questionnaire survey with 818 people living in these two districts, quantitative data of five-point likert scales were obtained to measure social dominance orientation, empathy and threat. Six hypotheses are tested by structural equation modeling with attained variables as social dominance, social egalitarianism, empathy, socioeconomic threat and politic threat in the related data. Based on the related theories, mainly Intergroup Contact Theory and Integrated Threat Theory, interactions of sub-dimensions of social dominance orientation (social dominance and social egalitarianism) and empathy with the perception towards Syrian refugees (socioeconomic threat and political threat) are analyzed comparatively for both districts.

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