Dynamic Reduct from Partially Uncertain Data Using Rough Sets

Author(s):  
Salsabil Trabelsi ◽  
Zied Elouedi ◽  
Pawan Lingras
Author(s):  
Vicenç Torra ◽  
Yasuo Narukawa ◽  
Masahiro Inuiguchi

The 6th International Conference on Modeling Decisions for Artificial Intelligence (MDAI) was held at Awaji Island, Japan, from November 30 to December 2, 2009 and was the inspiration for this special issue. The nine selected papers concern soft computing tool applications. The first, by Yoshida, studies the risk analysis of portfolios under uncertainty and gives expressions showing explicit relationships among parameters in a portfolio. The second, by Entani, proposes an efficiency-interval-based measure based on interval data envelopment analysis. The third, by Hamasuna, Endo, and Miyamoto, concerns clustering for data with tolerance and proposes algorithms for this type of data. The fourth, by Endo, Hasegawa, Hamasuna, and Kanzawa, focuses on fuzzy c-means clustering for uncertain data using quadratic regularization. The fifth, by Honda, Notsu, and Ichihashi, also involves clustering, focusing on variable selection/weighting in PCA-guided k-means. The sixth, by Hwang and Miyamoto, covers clustering focusing on kernel fuzzy c-means and some interesting new results. The seventh, by Kanzawa, Endo, and Miyamoto, uses fuzzy c-means in semisupervised fuzzy c-means. The eighth, by Kudo and Murai, is devoted to rough sets, proposing a heuristic algorithm for calculating a relative reduct candidate. The closing contribution, by Kusunoki and Inuiguchi, is also devoted to rough sets, with the authors studying rough set models in information tables with missing values. We thank the referees for their review work, and the Fuji Technology Press Ltd. staff for its encouragement and advice.


2021 ◽  
pp. 1-24
Author(s):  
Mengmeng Li ◽  
Chiping Zhang ◽  
Minghao Chen ◽  
Weihua Xu

Multi-granulation decision-theoretic rough sets uses the granular structures induced by multiple binary relations to approximate the target concept, which can get a more accurate description of the approximate space. However, Multi-granulation decision-theoretic rough sets is very time-consuming to calculate the approximate value of the target set. Local rough sets not only inherits the advantages of classical rough set in dealing with imprecise, fuzzy and uncertain data, but also breaks through the limitation that classical rough set needs a lot of labeled data. In this paper, in order to make full use of the advantage of computational efficiency of local rough sets and the ability of more accurate approximation space description of multi-granulation decision-theoretic rough sets, we propose to combine the local rough sets and the multigranulation decision-theoretic rough sets in the covering approximation space to obtain the local multigranulation covering decision-theoretic rough sets model. This provides an effective tool for discovering knowledge and making decisions in relation to large data sets. We first propose four types of local multigranulation covering decision-theoretic rough sets models in covering approximation space, where a target concept is approximated by employing the maximal or minimal descriptors of objects. Moreover, some important properties and decision rules are studied. Meanwhile, we explore the reduction among the four types of models. Furthermore, we discuss the relationships of the proposed models and other representative models. Finally, illustrative case of medical diagnosis is given to explain and evaluate the advantage of local multigranulation covering decision-theoretic rough sets model.


1999 ◽  
Vol 04 (01) ◽  
Author(s):  
C. Zopounidis ◽  
M. Doumpos ◽  
R. Slowinski ◽  
R. Susmaga ◽  
A. I. Dimitras

2012 ◽  
Vol 23 (7) ◽  
pp. 1745-1759 ◽  
Author(s):  
Qing-Hua ZHANG ◽  
Guo-Yin WANG ◽  
Yu XIAO
Keyword(s):  

2011 ◽  
Vol 34 (10) ◽  
pp. 1897-1906 ◽  
Author(s):  
Kun YUE ◽  
Wei-Yi LIU ◽  
Yun-Lei ZHU ◽  
Wei ZHANG

Sign in / Sign up

Export Citation Format

Share Document