Attribute dependency functions considering data efficiency

Advantage matrix: two novel multi-attribute decision-making methods and their applications

By comparing attributes of objects in an information system, the advantage matrix on the object set is established in this paper. The contributions can be identified as follows: (1) The advantage degree is proposed by the accumulation of the advantage matrix. (2) Based on the advantage matrix, the advantage (disadvantage) neighborhood approximation operator and the advantage (disadvantage) correlation approximation operator are defined and studied. Based on these two new operators, the neighborhood degree and the correlation degree are presented. The relationships between them are also investigated to demonstrate the value of the proposed method. (3) Finally, based on the above three degrees, new algorithms are designed, in which the effectiveness and robustness of the algorithms are analyzed by practical examples.

Heuristic Algorithm for Attribute Reduction Based on Classification Ability by Condition Attributes

The heuristic algorithm we propose to compute a relative reduct candidate is based on evaluating classification ability of condition attributes. Considering the discernibility and equivalence of objects for condition attributes in relative reducts, we introduce evaluation criteria for condition attributes and relative reducts. The computational complexity of the proposed algorithm isO(|U|2|C|2). Experimental results indicate that our algorithm often generates a relative reduct producing a good evaluation result.