Rule Induction Based on Rough Sets from Information Tables Containing Possibilistic Information

Abstract

A theoretical framework of rule induction based on rough sets is shown in possibilistic information system. Each object with non zero possible membership degrees possibly supports rules. On the other hand, every object with non zero certain membership degrees do not certainly support rules. We cannot know what rules are induced from rough approximations without considering characteristic value of objects. This leads to our formulating rough approximations under considering characteristic values of objects. Furthermore, we have introduced a criterion for valuable rules. Our approach is free from the restriction that possibilistic information of attributes in possibiilistic information systems.