计算机科学 ›› 2020, Vol. 47 ›› Issue (8): 137-143.doi: 10.11896/jsjkx.190700188
桑彬彬, 杨留中, 陈红梅, 王生武
SANG Bin-bin, YANG Liu-zhong, CHEN Hong-mei , WANG Sheng-wu
摘要： 在现实生活中, 数据不断累积增加, 原有准则和决策之间的相互关系也随之动态变化, 如何高效地计算属性约简是动态决策亟需解决的问题。增量更新方法可以有效地完成动态学习任务, 因为它可以在原有知识的基础上获取新的知识。文中利用优势粗糙集方法研究了在有优势关系的数据中添加单个对象时的增量属性约简方法。首先, 定义了优势集矩阵作为更新的目标, 用来计算新的优势条件熵;其次, 通过分析增加对象的3种不同情况, 提出了优势条件熵的增量学习机制;然后, 基于优势集矩阵设计了增量属性约简算法;最后, 对6种不同的UCI数据集进行实验, 用于比较增量和非增量算法的有效性和高效性。实验结果显示, 提出的增量属性约简算法不仅在有效性上与非增量属性约简算法保持一致, 而且在高效性上要远优于非增量属性约简算法。因此, 所提算法能有效且高效地完成动态优势关系数据中属性约简的任务。
中图分类号:
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