Geno-fuzzy classification trees

Making the non-terminal nodes of a binary tree classifier fuzzy can mitigate tree brittleness. Using a genetic algorithm, two optimization techniques are explored. In one case, each generation minimizes classification error by optimizing a common fuzzy percent, p_T, used to determine parameters at every node. In the other case, each generation yields a sequence of minimized node-specific parameters. The output value is determined through defuzzification after input vectors, in general, take both paths at each node with a weighting factor determined by the node membership functions. Experiments conducted using this geno-fuzzy approach yield an improvement compared with other classical algorithms.