Geno-fuzzy classification trees |
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Authors: | Richard E. Haskell [Author Vitae] Charles Lee [Author Vitae] [Author Vitae] |
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Affiliation: | a CSE Department, Oakland University, Rochester, MI 48309, USA b SAIC NASA Ames Research Center, Moffett Field, CA 94035, USA |
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Abstract: | 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, pT, 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. |
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Keywords: | Genetic Fuzzy Decision tree Classification Fuzzy weights |
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