Incomplete decision contexts: approximate concept construction, rule acquisition and knowledge reduction. (English) Zbl 1266.68172

Li, Jinhai; Mei, Changlin; Lv, Yuejin

Int. J. Approx. Reasoning 54, No. 1, 149-165 (2013).

Summary: Incomplete decision contexts are a kind of decision formal contexts in which information about the relationship between some objects and attributes is not available or is lost. Knowledge discovery in incomplete decision contexts is of interest because such databases are frequently encountered in the real world. This paper mainly focuses on the issues of approximate concept construction, rule acquisition and knowledge reduction in incomplete decision contexts. We propose a novel method for building the approximate concept lattice of an incomplete context. Then, we present the notion of an approximate decision rule and an approach for extracting non-redundant approximate decision rules from an incomplete decision context. Furthermore, in order to make the rule acquisition easier and the extracted approximate decision rules more compact, a knowledge reduction framework with a reduction procedure for incomplete decision contexts is formulated by constructing a discernibility matrix and its associated Boolean function. Finally, some numerical experiments are conducted to assess the efficiency of the proposed method.

MSC:

68T30	Knowledge representation
68T37	Reasoning under uncertainty in the context of artificial intelligence

Keywords:

formal concept analysis; rough set theory; incomplete context; incomplete decision context; rule acquisition; knowledge reduction