Discovering probabilistic decision rules

Abstract

Techniques to generate probabilistic decision rules are presented that are used to forecast or measure the competitiveness of companies. Rules estimating the competitiveness of companies are described and the generated rules are then applied to forecast the competitiveness of previously unseen companies. Experimental results show that probabilistic decision rule technique outperforms many other machine learning and statistical techniques in this application domain. These findings are further confirmed in a second application, the classification of credits into either good or bad. © 1997 John Wiley & Sons, Ltd.