The Method for Data Reduction Based on Evaluation of Attribute Significance

Chao-bo He, Qimai Chen
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引用次数: 1

Abstract

According to the problem of attribute subset selection,the paper put forward a method based on evaluation of attribute significance.Based on the rough set theories the method first defined the calculation formula of attribute significance and designed the corresponding solution algorithm,whose running time complexity decreased about |U|2 orders of magnitude comparing with the similar algorithm on the same test dataset with |U| rescords.The result of application example shows that this method can reserve the condition attributes,which are important for decision attributes,and also can perform the data reduction operation effectively.
基于属性显著性评价的数据约简方法
针对属性子集选择问题,提出了一种基于属性重要性评价的属性子集选择方法。该方法基于粗糙集理论,首先定义了属性重要度的计算公式,并设计了相应的求解算法,与相同测试数据集上的同类算法相比,该算法的运行时间复杂度降低了约2个数量级。应用实例表明,该方法既能保留决策属性中重要的条件属性,又能有效地进行数据约简操作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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