摘要
Rough Set is a valid mathematical theory developed in recent years, which hasthe ability to deal with imprecise, uncertain, and vague information. This paper presents a newincremental rule acquisition algorithm based on rough set theory. First, the relation of the newinstances with the original rule set is discussed. Then the change law of attribute reduction andvalue reduction are studied when a new instance is added. Follow, a new incremental learningalgorithm for decision tables is presented within the framework of rough set. Finally, the newalgorithm and the classical algorithm are analyzed and compared by theory and experiments.
Rough Set is a valid mathematical theory developed in recent years, which hasthe ability to deal with imprecise, uncertain, and vague information. This paper presents a newincremental rule acquisition algorithm based on rough set theory. First, the relation of the newinstances with the original rule set is discussed. Then the change law of attribute reduction andvalue reduction are studied when a new instance is added. Follow, a new incremental learningalgorithm for decision tables is presented within the framework of rough set. Finally, the newalgorithm and the classical algorithm are analyzed and compared by theory and experiments.
基金
This work is supported by National Science Foundation of China (No.60373111).