@inproceedings{d49718e269ee4d8eb1180e8d9c8b9b1e,
title = "Variable precision dominance based rough set model and reduction algorithm for preference-ordered data",
abstract = "Dominance-based rough set model has proven to be a powerful mathematical tool for preference-ordered information system. We define some measures of roughness of approximation and conclude that the definition of lower and upper approximations is not robust to noise sample in the former approaches, and then an extended model is presented based on majority inclusion. Some measures are introduced to calculate the accuracy and quality of approximation using variable precision dominance rough set methodology. The quality of approximation of partition is used as a measure of the significance of attributes. Based on the measure, the definitions of dependency of attribute set, redundancy of attribute, reduct and core are given. A greedy algorithm is constructed for preference-ordered data reduction.",
keywords = "Dominance based rough set, Preference-ordered data, Reduction algorithm, Variable precision",
author = "Hu, \{Qing Hua\} and Yu, \{Da Ren\}",
year = "2004",
doi = "10.1109/ICMLC.2004.1382179",
language = "英语",
isbn = "0780384032",
series = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2279--2284",
booktitle = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",
address = "美国",
note = "3rd International Conference on Machine Learning and Cybernetics, ICMLC 2004 ; Conference date: 26-08-2004 Through 29-08-2004",
}