Geno-fuzzy classification trees
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摘要
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.
论文关键词:Genetic,Fuzzy,Decision tree,Classification,Fuzzy weights
论文评审过程:Received 22 October 2002, Revised 17 November 2003, Accepted 9 January 2004, Available online 15 April 2004.
论文官网地址:https://doi.org/10.1016/j.patcog.2004.01.010