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.
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