Using Learning of Behavior Rules to Mine Medical Data for Sequence Rules
In fields like medical care the temporal relations in the records (transactions) are of great help for identifying a particular group of cases. Thus there is some need for sequence rule learning in the classification problems in these fields. In this paper, a genetic algorithm for sequence rule learning is presented based on concepts from learning behavior of agents. The algorithm employs a Michigan-like approach to evolve a group of sequence rules, and extracts good ones into the result sequence rule set from time to time. It contains a novel quality-based intelligent genetic operator, and many adaptive enhancements to make implicit use of data-set-specific knowledge. The algorithm is evaluated on a real-world medical data set from the PKDD 99 Challenge. The results indicate that the algorithm can get satisfactory sequence rule sets from the sparse and noisy data set.