Rule refinement with extended data expression
Jung Min Kong, Dong-Hun Seo, Keon Myung Lee · 2007
The rule refinement problem has been known to be one of the most difficult and complex problems. This paper presents a systematic rule refinement method that deals with the old rule directly with the new data, for the first time. To be able to do the rule refinement, the data are represented in the extended data expression, where an event has its weight of importance. To show how this can be done systematically, a decision tree classifier is used for the rule refinement. The weights of the events of the former rule are adjusted according to the depth of the tree merged with the collected new data set to form the new rule. Experiment shows that this approach, with properly designing the weight assignment procedure, is promising to enhance the performance of the inference engine by generating a rule with higher accuracy than the one from new data set only.