@inproceedings{3cd148e3e3b34fbe914a04ffc5fd8b74,
title = "Reduction algorithms for hybrid data based on fuzzy rough set approaches",
abstract = "Classical rough set theory is a powerful tool for nominal data. It has been generalized to fuzzy case with fuzzy indiscernibility relation, which is much general for real-world application. In this paper we introduce and extend Yager's entropy measure. And the definition of conditional entropy is interpreted as the increment of discernibility power by introducing an unseen attribute which is used as a significance measure of the attribute in rough set theory framework. We give novel definitions of independence, redact, and relative reduct based on the entropy measure in fuzzy rough set model. Then two greedy algorithms are proposed for computing reduct and relative reduct, respectively. Two illustrative examples show the proposed approaches are efficient.",
keywords = "Entropy, Fuzzy-rough set, Hybrid data, Reduction",
author = "Hu, \{Qing Hua\} and Yu, \{Da Ren\} and Xie, \{Zong Xia\}",
year = "2004",
doi = "10.1109/ICMLC.2004.1382005",
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 = "1469--1474",
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",
}