A new framework for incremental rule induction based on rough sets
A new framework for incremental rule induction based on rough sets
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DOI：
10.1109/grc.2011.6122679
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
S. Tsumoto
中科院分区：
文献类型：
--
作者：
S. Tsumoto
本文提出了一种新的增量学习框架的基础上的准确性和覆盖率。将实例的加法分为四种情况，得到了两个关于精度和覆盖率的不等式。该方法利用所得到的不等式将公式集分为规则层、子规则层和非规则层。然后，子规则层在规则更新中起核心作用。
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.