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Using phenotypic sharing in a classifier tool

机译:在分类器工具中使用表型共享

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This paper describes a classifier tool that uses a genetic algorithm to make rule induction. The genetic algorithm uses the Michigan approach, is domain independent and is able to process continuous and discrete attributes. Some optimizations include the use of phenotypic sharing (with linear complexity) to direct the search. The results of accuracy are compared with other 33 algorithms in 32 datasets. The difference of accuracy is not statistically significant at the 10percent level when compared with the best of the other 33 algorithms. The implementation allows the configuration of many parameters, and intends to be improved with the inclusion of new operators.
机译:本文介绍了一种使用遗传算法进行规则诱导的分类器工具。遗传算法使用密歇根州的方法,是域独立的,能够处理连续和离散的属性。一些优化包括使用表型共享(具有线性复杂性)来指导搜索。将精度的结果与32个数据集中的其他33种算法进行比较。与其他33算法中最好的相比,精度的差异在10平方水平时没有统计学意义。该实现允许配置许多参数,并在包含新的运算符时旨在改进。

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