A new framework for incremental rule induction based on rough sets
Shusaku Tsumoto · 2011
This paper proposes a new framework for incremental learning based on accuracy and coverage. Classified addition of example into four cases, two inequalities for accuracy and coverage are obtained. The proposed method classifies a set of formulae into three layers: rule layer, subrule layer and non-rule layer by using the inequalities obtained. Then, subrule layer plays a central role in updating rules.