Cost-sensitive Global Model Trees applied to loan charge-off forecasting

摘要

Regression learning methods in real world applications often require cost minimization instead of the reduction of various metrics of prediction errors. Currently in the literature, there is a lack of white box solutions that can deal with forecasting problems where under-prediction and over-prediction errors have different consequences. To fill this gap, we introduced the Cost-sensitive Global Model Tree (CGMT), which applies a fitness function that minimizes an average misprediction cost. Proposed specialized genetic operators improve searching for optimal tree structure and cost-sensitive linear regression models in the leaves. Experimental validation is performed on loan charge-off data. It is known to be a difficult forecasting problem for banks due to the asymmetric cost structure. Obtained results show that specialized evolutionary algorithm applied to model tree induction finds significantly more accurate predictions than tested competitors. Decisions generated by the CGMT are simple, easy to interpret, and can be applied directly.