Sampling strategies for targeting rare groups from a bank customer database

Abstract

This paper presents various balanced sampling strategies for building decision trees in order to target rare groups. A new coefficient to compare targeting performances of various learning strategies is introduced. A real life application of targeting specific bank customer group for marketing actions is described. Results shows that local sampling on the nodes while constructing the tree requires small samples to achieve the performance of processing the complete base, with dramatically reduced computing times.