一种基于GP的大规模数据分类方法

Sichun Wang, Yanhui Wu
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摘要

本文阐述了利用遗传规划(GP)在数据挖掘中创建和使用集成的方法。鉴于其在数据中复杂非线性模型中的表示能力,GP被认为在学习数据中的各种模式方面是有效的。由于不同的模型捕获不同的数据关系,GP模型非常适合集成中的组合。实验结果表明,不同的GP模型在函数形式和定义模型的变量方面都不相同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Large-Scale Data Classifying Approach Based on GP
he method that the utility of genetic programming (GP) is used to create and use ensembles in data mining is demonstrated in the paper . Given its representational power in the model of complex non-linearities in the data, GP is seen to be effective at learning diverse patterns in the data. With different models capturing varied data relationships, GP models are ideally suited for combination in ensembles. Experimental results show that different GP models are dissimilar both in terms of the functional form as well as with respect to the variables defining the models.
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