This paper proposes a new algorithm for target selection. This algorithm collects all frequent patterns (equivalent to frequent item sets) in a training set. These patterns are stored e?ciently using a compact data structure called a trie. For each pattern the relative frequency of the target class is determined. Target selection is achieved by matching the candidate records with the patterns in the trie. A score for each record results from this matching process, based upon the frequency values in the trie. The records with the best score values are selected. We have applied the new algorithm to a large data set containing the results of a number of mailing campaigns by a Dutch charity organization. Our algorithm turns out to be competitive with logistic regression and superior to CHAID.
|Title of host publication
|Proceedings of the 13th Belgium-Netherlands Conference on Artificial Intelligence
|B. Kröse, M. Rijke, de, G. Schreiber, M. Someren, van
|Place of Publication
|Published - 2001