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The generated rule base is further optimized by the descent method (DM).
From the generated rule template, a rule instance is generated with the actual sensors and actuators.
Finally, the effectiveness of the generated rule base has been validated using the simulator in autopilot mode.
The generated rule base is checked and optimized based on the similarity measures among the input fuzzy sets.
The generated rule is f rightarrow )post, where post is the postfix of the found sequence with prefix (f).
One common rule mining approach is to filter the generated rule set via constraints on 'interestingness' measures until its size becomes manageable [12, 24, 35].
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However, the number of generated rules is generally very high and must be filtered according to user-defined criteria and usefulness measures.
Most generated rules, however, are either redundant or insignificant.
Relying on that, the number of generated rules is very large too.
The classification accuracy and interpretability of generated rules are of major importance in fuzzy classification systems.
They consider the utility based temporal association rule mining method for generating the association rules and PSO is used to optimize the generated rules.
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