Abstract
We present CoLe, a cooperative, distributed model for mining
knowledge from heterogeneous data. CoLe allows for the cooperation of
different learning algorithms and the combination of the mined knowledge into
knowledge structures that no individual learner can produce. CoLe organizes
the work in rounds so that knowledge discovered by one learner can help others
in the next round. We implemented a CoLe-based system for mining diabetes
data, including a genetic algorithm for learning event sequences, improvements
to the PART algorithm for our problem and combination methods to produce
hybrid rules containing conjunctive and sequence conditions. In our
experiments, the CoLe-based system outperformed the individual learners, with
better rules and more rules of a certain quality. Our improvements to
learners also showed the ability to find useful rules. From the medical
perspective, our system confirmed hypertension has a tight relation to
diabetes, and it also suggested connections new to medical doctors.
Notes
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