针对现有不完备决策表属性约简算法复杂度较高的问题,提出了基于属性分辨度的属性约简算法. 文中分析了不完备决策表中条件属性相对于决策重要性的外在表现,提出了属性分辨度的概念,并给出了属性分辨度随着约简属性集的变化而动态更新的计算方法. 该算法在属性约简过程中会不断删除已经属于正域的对象或不影响正域计算的相容块,通过降低样本数据的规模来减少计算耗时,加速属性约简. 理论分析和仿真实验表明,文中算法是有效的,并且算法复杂度优于现有的不完备决策表属性约简算法.

Proposed in this paper is a novel attribute reduction algorithm based on the attribute discernibility to re-duce the high computational complexity of the existing attribute reduction algorithms for incomplete decision tables.In the investigation, the external manifestation of condition attribute significance relative to the decision attribute inincomplete decision tables is analyzed, the attribute discernibility is defined, and the calculating method of dynamicupdate of attribute discernibility with the change of reduction subset is proposed. During the attribute reduction, thealgorithm continuously deletes the objects belonging to the positive domain or the consistent blocks having no effecton the positive domain calculation. Thus, the data scale and the time consumption decreases, and the attribute re-duction quickens. Theoretical analyses and simulation experiments demonstrate that the proposed attribute reductionalgorithm for incomplete decision tables is effective and is superior to the existing algorithms in terms of time com-plexity.