ITM Web Conf.
Volume 11, 20172017 International Conference on Information Science and Technology (IST 2017)
|Number of page(s)||10|
|Section||Session I: Computational Intelligence|
|Published online||23 May 2017|
Attribute Reduction Algorithm Based on Structure Discernibility Matrix in Composite Information Systems
1 College of Information Science and Engineering, Hunan University, Changsha 410082, China
2 College of Mathematics and Econometric, Hunan University, Changsha 410082, China
a Corresponding author: firstname.lastname@example.org
Attribute reduction, as an important preprocessing step for knowledge acquiring in data mining, is one of the key issues in rough set theory. It can only deal with attributes of a specific type in the information system by using a specific binary relation. However, there may be attributes of multiple different types in information systems in real-life applications. A composite relation is proposed to process attributes of multiple different types simultaneously in composite information systems. In order to solve the time-consuming problem of traditional heuristic attribute reduction algorithms, a novel attribute reduction algorithm based on structure discernibility matrix was proposed in this paper. The proposed algorithms can choose the same attribute reduction as its previous version, but it can be used to accelerate a heuristic process of attribute reduction by avoiding the process of intersection and adopting the forward greedy attribute reduction approach. The theoretical analysis and experimental results with UCI data sets show that the proposed algorithm can accelerate the heuristic process of attribute reduction.
© Owned by the authors, published by EDP Sciences, 2017
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