Attribute reduction plays an important role in knowledge discovery and data mining. Confronted with data characterized by the interval and missing values in many data analysis tasks, it is interesting to research the attribute reduction for interval-valued data with missing values. Uncertainty measures can supply efficient viewpoints, which help us to disclose the substantive characteristics of such data. Therefore, this paper addresses the attribute reduction problem based on uncertainty measure for interval-valued data with missing values. At first, an uncertainty measure is provided for measuring candidate attributes, and then an efficient attribute reduction algorithm is developed for the interval-valued data with missing values. To improve the efficiency of attribute reduction, the objects that fall within the positive region are deleted from the whole object set in the process of selecting attributes. Finally, experimental results demonstrate that the proposed algorithm can find a subset of attributes in much shorter time than existing attribute reduction algorithms without losing the classification performance.

中文翻译：

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一种有效的基于不确定性度量的具有缺失值的区间值数据的属性约简方法

属性约简在知识发现和数据挖掘中起着重要作用。面对许多数据分析任务中以区间和缺失值为特征的数据，研究具有缺失值的区间值数据的属性约简是很有趣的。不确定性度量可以提供有效的观点，这有助于我们揭示这些数据的实质性特征。因此，本文针对具有缺失值的区间值数据解决了基于不确定性测度的属性约简问题。首先，为度量候选属性提供了一种不确定性度量，然后针对具有缺失值的区间值数据开​​发了一种有效的属性约简算法。为了提高属性约简的效率，在选择属性的过程中，将落在正区域内的对象从整个对象集中删除。最后，实验结果表明，与现有的属性约简算法相比，该算法可以在更短的时间内找到属性子集，而不会损失分类性能。