The improvement of response modeling: combining rule-induction and case-based reasoning
Direct mail is a typical example for response modeling to be used. In order to decide which people will receive the mailing, the potential customers are divided into two groups or classes (buyers and non-buyers) and a response model is created. Since the improvement of response modeling is the purpose of this paper, we suggest a combined approach of rule-induction and case-based reasoning. The initial classification of buyers and non-buyers is done by means of the C5-algorithm. To improve the ranking of the classified cases, we introduce in this research rule-predicted typicality. The combination of these two approaches is tested on synergy by elaborating a direct mail example.
response modeling case-based reasoning direct mail typicality c4.5 algorithm data mining