摘要
现实生活中许多数据库都是动态变化的,为了获取新的知识,传统的方法需要重复计算,耗时巨大。为了克服这个缺陷,有效处理动态数据,许多学者提出了增量学习方法。针对决策表属性值动态变化,提出了基于属性值细化的矩阵增量约简算法,当一部分属性值被细化时,同非增量约简方法相比,增量方法能快速找到新的约简,最后通过UCI数据进行性能测试,实验仿真结果表明所提增量约简算法是有效的。
In practices, many real data in databases may vary dynamically. One has to run a knowledge acquisition method repeatedly in order to acquire new knowledge. This is very time-consuming. To overcome this deficiency, incremental approaches have been presented to deal with dynamic data set. This paper proposes a matrix-based incremental reduction approach with attribute values refining. When a part of data in a given data set is replaced by some new data, compared with the non-incremental reduction approach, the developed incremental reduction approach can find a new reduct in a much shorter time. Finally, experiments on two data sets downloaded from UCI show that the developed algorithm is effective.
出处
《计算机工程与应用》
CSCD
北大核心
2017年第21期68-71,76,共5页
Computer Engineering and Applications
基金
国家自然科学基金联合项目(No.U1230117)
关键词
属性值细化
增量学习
属性约简
粗糙集
知识粒度
attribute values refining
incremental learning
attribute reduction
rough set
knowledge granularity
