摘要
从 Rough set理论出发 ,讨论在新增数据时 ,新数据与已有规则集的关系、属性约简以及值约简的变化规律 .并在此基础上提出一个新的基于 Rough Set理论的增量式算法 .从理论上和实验上对新算法和传统算法在算法复杂度上做了分析与比较 .
The relation of the new instances with the originally rule set, the change law of attribute reducti on and value reduction were studied when a new instance coming. A new in c remental learning algorithm for decision tables was presented within the framewo r k of rough set. The new algorithm and the classical algorithm were analy zed and compared by theory and experiments.
出处
《小型微型计算机系统》
CSCD
北大核心
2005年第1期36-41,共6页
Journal of Chinese Computer Systems
基金
国家自然科学基金 (60 3 73 111)资助
重庆市科委攻关基金 (70 0 6)资助
重庆邮电学院科研基金 (A2 0 0 4-4 6)资助
