Fuzzy partition and input selection by genetic algorithms for designing fuzzy rule-based classification systems
The resolution of each axis is determined by the histogram of training patterns.
The membership function is determined by the histogram and a genetic algorithm.
Input selection is also performed by the genetic algorithm.
We show the effectiveness of the proposed method by computer simulations on iris data with 4 attributes and wine data with 13 attributes.
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