Rough set-based approaches for discretization: a compact review-Reference-Cited by-同舟云学术

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Rough set-based approaches for discretization: a compact review

Published:2015-02-04 Issue:2 Volume:44 Page:235-263
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Ali Rahman,Siddiqi Muhammad Hameed,Lee Sungyoung

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

http://link.springer.com/content/pdf/10.1007/s10462-014-9426-2.pdf

Reference63 articles.

1. Alcalá-Fdez J, Sánchez L, García S, Jesus MJd, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM, Fernández JC, Herrera F (2009) KEEL: a software tool to assess evolutionary algorithms to data mining problems. Soft Comput 13(3):307–318. doi: 10.1007/s00500-008-0323-y

2. Bakar AA, Othman ZA, Shuib NLM (2009) Building a new taxonomy for data discretization techniques. In: 2nd conference on data mining and optimization DMO’09, IEEE, pp 132–140

3. Batu T, Dasgupta S, Kumar R, Rubinfeld R (2005) The complexity of approximating the entropy. SIAM J Comput 35(1):132–150

4. Bazan J, Szczuka M (2005) The rough set exploration system. Trans Rough Sets III:25–42

5. Blajdo P, Grzymala-Busse J, Hippe Z, Knap M, Mroczek T, Piatek L (2008) A comparison of six approaches to discretization—a rough set perspective. Rough Sets Knowl Technol, 31–38

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