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1. An Application Of Feature Selection For The Fuzzy Rule Based Classifier Design With The Order Based Semantics Of Linguistic Terms For High—Dimensional Datasets / Pham Dinh Phong // Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics . - 2015. - P. 171-184. - ISSN:
14 p.
Ký hiệu phân loại (DDC): 005
This paper presents an approach to tackle the high-dimensional dataset problem for the hedge algebras based classification method proposed in N. C. Ho, D.T. Pedrycz, D. T. Long, and T. T. Son. “A genetic design of linguistic terms for fuzzy rule based classifiers." International Journal of Approximate Reasoning, vol. 54, no. 1. pp. 1 21, 2013 by utilizing the featureselection algorithm proposed in X. Sun, Y. Liu, M. Xu,H. Chen, J. Han, and K. Wang, “Feature selection using dynamic weightsfor classification," Knowledgr--Based Systems, vol. 37, pp. 541—549, 2013.Theproposed method is also compared with three classical classification methods based on the statisticaland probabilistic approaches showing that it is a robust classifier.
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An Effective Algorithm For Computing Re An Ef Decision Tables Ducts In Decision Tables / Do Si Truong, Lam Thanh Hien, Nguyen Thanh Tung // Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics . - 2022. - tr. 65-80. - ISSN: 1813-9663
Ký hiệu phân loại (DDC): 512
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
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