一种新的基于粗集的增量式规则提取算法

一种新的基于粗集的增量式规则提取算法 A New Incremental Rule-Extracting Algorithm Based on Rough Sets 左敏 ZUO Min 1 2 first-author 郭成城 GUO Cheng-cheng 1 2 晏蒲柳 YAN Pu-liu 1 2 武汉大学电子信息学院 武汉大学电子信息学院 School of Electronic Information,Wuhan University School of Electronic Information,Wuhan University 武汉大学电子信息学院,湖北,武汉,430072 武汉大学电子信息学院,湖北,武汉,430072 通过引入属性值通配符,进而将规则表示为带通配符的样本——规则样本,并对粗集的知识约简方法稍作修改,使得加入新样本后更新规则库时能够充分利用已经获得的规则,尽量减小待约简的决策表的数据量,避免每次从庞大的原始决策表开始约简,从而加快更新速度,减小计算量,只作少量修改,而不必再从头约简.最后结合一个实例阐明了该方法的基本思路. By defining the attribute-value wildcard, each rule is expressed in a form called "Rule Sample", which contains wildcards （as compared with the original "experimental sample",with a definite value for each attribute ）. Then with the classical RS rule-extracting algorithm slightly modified, rules can be easily refreshed whenever new samples are added to the decision table, because the application of wildcard can help to avoid processing from the original decision table（usually contains extremely large amount of da ta） every time. Therefore time and efforts are saved greatly when the rules need to be refreshed. An example is given to demonstrate the main idea of this improved algorithm. 粗集 决策表 规则 增量式学习 rough sets decision table rule incremental learning TP393.01 国家自然科学基金资助项目（69896240） 2002-03-01 2021-04-01 3