Vui lòng dùng định danh này để trích dẫn hoặc liên kết đến tài liệu này: https://dspace.ctu.edu.vn/jspui/handle/123456789/109547
Nhan đề: An effective algorithm for computing reducts in decision tables
Tác giả: Do, Si Truong
Lam, Thanh Hien
Nguyen, Thanh Tung
Từ khoá: Feature selection
Attribute reduction
Attribute clustering
Partitioning around medoids clustering
Normalized variation of information
Rough set
Năm xuất bản: 2022
Tùng thư/Số báo cáo: Journal of Computer Science and Cybernetics;Vol.38, No.03 .- P.277-292
Tóm tắt: Attribute reduction is one important part researched in rough set theory. A reduct from a decision table is a minimal subset of the conditional attributes which provide the same information for classification purposes as the entire set of available attributes. The classification task for the high dimensional decision table could be solved faster if a reduct, instead of the original whole set of attributes, is used. In this paper, we propose a reduct computing algorithm using attribute clustering. The proposed algorithm works in three main stages. In the first stage, irrelevant attributes are eliminated. In the second stage relevant attributes are divided into appropriately selected number of clusters by Partitioning Around Medoids (PAM) clustering method integrated with a special metric in attribute space which is the normalized variation of information. In the third stage, the representative attribute from each cluster is selected that is the most class-related. The selected attributes form the approximate reduct. The proposed algorithm is implemented and experimented. The experimental results show that the proposed algorithm is capable of computing approximate reduct with small size and high classification accuracy, when the number of clusters used to group the attributes is appropriately selected.
Định danh: https://dspace.ctu.edu.vn/jspui/handle/123456789/109547
ISSN: 1813-9663
Bộ sưu tập: Tin học và Điều khiển học (Journal of Computer Science and Cybernetics)

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