Constructing importance measure of attributes in covering decision table
In rough set theory, attributes importance measure is a crucial factor in applications of attribute reduction and feature selection. Many importance measure methodologies for discrete-valued information system or decision table have been developed. However, there are only limited studies on importance measurement for numerical-valued information system. In this paper, knowledge change-based importance measure, with the structural characteristics of fuzzy measure, is introduced to evaluate the importance of attributes in covering decision table. We first present the concept of similarity block in attribute space, based on which coverings are induced to construct the lower and upper approximation operators in covering-based rough sets. In particular, the traditional importance measure is extended to deal with covering decision table. Further, an evaluation model based on the knowledge change-based importance measure is constructed. Experiments are conducted on the public data sets from UCI, and a case study on the students overall evaluation is given finally. Theoretical analysis and experimental results show that the proposed importance measure is effective for evaluating the importance of attributes in covering decision table.
importance measure covering decision table rough set theory evaluation model