【摘要】 为解决动态环境下数值型偏序关系数据的属性约简问题，利用优势粗糙集的区分度提出一种增量式属性约简算法。在数值型信息系统环境下，定义邻域优势区分度度量，通过邻域优势区分度设出一种非增量式属性约简算法；研究和分析对象变化场景下邻域优势区分度进行增量式更新的原理；分别提出数据对象增加和减少情形下数据集属性约简的增量式更新算法。在多个UCI数据集上进行实验验证，实验结果表明，该增量式算法能够有效完成动态数据的属性约简任务。更多还原

【Abstract】 To solve the problem of attribute reduction for numerical ordered data in dynamic environments, an incremental attri-bute reduction algorithm was proposed using the discrimination measurement of dominance rough set. In a numerical information system environment, a neighborhood dominance discrimination measurement was defined, and a non-incremental attribute reduction algorithm was designed based on the neighborhood dominance discrimination measurement. The principle of incremental updating of neighborhood dominance discrimination measurement in object changing scenarios was studied and analyzed. When adding or deleting batch objects in a numerical information system, an incremental updating algorithm for attribute reduction was proposed respectively. Experimental results on several UCI datasets show that the incremental algorithm can effectively accomplish the task of attribute reduction of dynamic data.更多还原