Feature transformation methods in data mining

被引：69
作者：
Kusiak, A [1 ]
机构：
[1] Univ Iowa, Dept Mech & Ind Engn, Intelligent Syst Lab, Iowa City, IA 52242 USA
来源：
IEEE TRANSACTIONS ON ELECTRONICS PACKAGING MANUFACTURING | 2001年 / 24卷 / 03期
关键词：
classification; data mining; decision making; feature bundling; feature transformation method; knowledge discovery; transformed data set;
D O I：
10.1109/6104.956807
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The quality of knowledge extracted from a data set can be enhanced by its transformation. Discretization and filling missing data are the most common forms of data transformation. A new transformation method named feature bundling is introduced. A feature bundle involves a set of features in its pure or transformed form. The computational results reported in this paper show that the classification accuracy of decision rules generated from data sets with feature bundles is enhanced. The proposed concept of feature bundling is applied to a data set from semiconductor industry.
引用
收藏
页码：214 / 221
页数：8
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