Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks
Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks
复制标题
基于遗传编程的机器学习和知识发现任务特征构建
DOI：
--
复制
发表时间：
2002
影响因子：
2.6
通讯作者：
K. Krawiec
中科院分区：
文献类型：
--
作者：
K. Krawiec
在本文中，我们使用遗传编程来改变机器学习器的输入数据的表示。特别地，这里感兴趣的主题是从示例中学习范例中的特征构造，其中新特征基于原始属性集构建。本文首先介绍了基于GP的特征构造的一般框架。然后，提出了一种扩展的方法，在进化过程中保留有用的组件（功能），而不是标准的方法，在搜索过程中，有价值的功能往往会丢失。最后，我们提出并讨论了一个广泛的计算实验的结果进行了几个参考数据集。实验结果表明，使用GP构造的特征丰富的表示诱导的分类器在测试集上提供了更好的分类精度。特别是，扩展的方法，提出的文件被证明是能够优于标准的方法在一些基准问题上的统计显着水平。
In this paper we use genetic programming for changing the representation of the input data for machine learners. In particular, the topic of interest here is feature construction in the learning-from-examples paradigm, where new features are built based on the original set of attributes. The paper first introduces the general framework for GP-based feature construction. Then, an extended approach is proposed where the useful components of representation (features) are preserved during an evolutionary run, as opposed to the standard approach where valuable features are often lost during search. Finally, we present and discuss the results of an extensive computational experiment carried out on several reference data sets. The outcomes show that classifiers induced using the representation enriched by the GP-constructed features provide better accuracy of classification on the test set. In particular, the extended approach proposed in the paper proved to be able to outperform the standard approach on some benchmark problems on a statistically significant level.