基于概念格的多层属性约简方法

Multi-Level Attribute Reduction Methods Based on Concept Lattice

属性约简是粗糙集理论中的核心问题之一,概念格是进行知识表示和数据分析的一种有效工具.文中利用概念格作为约简工具,给出基于概念格的多层属性约简算法,提出相融可辨概念、相融等价概念、亏n级等概念,研究内涵亏值对分类能力变化产生的影响,给出概念格中形式背景约简的判定定理.文中算法能完备地求出所有可约简的最大属性集合,从而为概念格中属性约简提供一种有效方法.最后,通过实例分析和实验对比说明该约简算法的可行性与有效性.

Attribute reduction is the kernel contents of rough set theory. Concept lattice is effective for knowledge representation and data analysis. Multi-level attribute reduction algorithm based on concept lattice is proposed by using concept lattice as reduction tool. The concepts including discriminable concepts, equivalent concepts and wane-n level are also introduced. The infuence of intent waned-value producing impact on the change of classification ability and the judge theorems of attribute reduction in concept lattice are mainly studied. The proposed algorithm discovers all the maximal reductions completely and an effective approach is presented to attribute reduction in concept lattice. Finally, a real example and experiment comparisons demonstrate both its feasibility and effectiveness.