Incremental rules induction based on rule layers

Authors:
Shusaku Tsumoto;Shoji Hirano
Affiliations:
Department of Medical Informatics, School of Medicine, Faculty of Medicine, Shimane University, Izumo, Japan;Department of Medical Informatics, School of Medicine, Faculty of Medicine, Shimane University, Izumo, Japan
Venue:
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Year:
2012

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Abstract

This paper proposes a new framework for incremental learning based on accuracy and coverage. Classification of addition of example into four cases gives two inequalities for accuracy and coverage. 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. The proposed method was evaluated on a dataset on meningitis, whose results show that it outperforms other conventional rule induction methods.