Framework for efficient feature selection in genetic algorithm based data mining
Framework for efficient feature selection in genetic algorithm based data mining
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DOI：
10.1016/j.ejor.2006.02.040
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发表时间：
2007-07
期刊：
影响因子：
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通讯作者：
R. Sikora;S. Piramuthu
中科院分区：
文献类型：
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作者：
R. Sikora;S. Piramuthu
我们提出了设计更有效和高效的遗传算法为基础的数据挖掘技术，使用的概念特征选择。显式特征选择传统上是作为一个包装器的方法，其中每个候选特征子集是通过执行该子集上的数据挖掘算法进行评估。在这篇文章中，我们提出了一个遗传算法的挖掘和功能选择的任务，同时通过发展一个二进制代码沿着侧的染色体结构用于发展的规则。然后，我们提出了一个包装方法的特征选择的基础上Hausdorff距离测量。将上述技术应用于真实的世界数据挖掘问题的结果表明，结合这两种特征选择方法在预测精度和计算效率方面提供了最佳性能。
We present the design of more effective and efficient genetic algorithm based data mining techniques that use the concepts of feature selection. Explicit feature selection is traditionally done as a wrapper approach where every candidate feature subset is evaluated by executing the data mining algorithm on that subset. In this article we present a GA for doing both the tasks of mining and feature selection simultaneously by evolving a binary code along side the chromosome structure used for evolving the rules. We then present a wrapper approach to feature selection based on Hausdorff distance measure. Results from applying the above techniques to a real world data mining problem show that combining both the feature selection methods provides the best performance in terms of prediction accuracy and computational efficiency.