A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
A greedy genetic algorithm for the quadratic assignment problem
Computers and Operations Research
An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Probabilistic and discriminative group-wise feature selection methods for credit risk analysis
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Classification and regression models are widely used by mainstream credit granting institutions to assess the risk of customer default. In practice, the objectives used to derive model parameters and the business objectives used to assess models differ. Models parameters are determined by minimising some function or error or by maximising likelihood, but performance is assessed using global measures such as the GINI coefficient, or the misclassification rate at a specific point in the score distribution. This paper seeks to determine the impact on performance that results from having different objectives for model construction and model assessment. To do this a genetic algorithm (GA) is utilized to generate linear scoring models that directly optimise business measures of interest. The performance of the GA models is then compared to those constructed using logistic and linear regression. Empirical results show that all models perform similarly well, suggesting that modelling and business objectives are well aligned.