@inproceedings{37b727b0cbe948cdabfd337b1d248f08,
title = "Granular entropy based hybrid knowledge reduction using uniform rough approximations",
abstract = "Knowledge reduction is usually a key pre-processing step before some other action such as induction of rules is performed. Rough set theory is a powerful tool to deal with knowledge reduction. The values of attributes in real world may often be both symbolic and real-valued, that is, hybrid. In order to deal with the reduction of the hybrid knowledge, we analyze Pawlak's rough approximations and its different kinds of extensive versions and then obtain a uniform form of knowledge granules and rough approximations under crisp and fuzzy relations. Aimed at hybrid knowledge reduction using the uniform rough approximations, we give a new interpretation to Yager's entropy from {"}knowledge granules{"} and present the concept and definitions of {"}granular entropy{"}. Based on the granular entropy, we propose an approach to hybrid knowledge reduction. The utility of this approach is demonstrated with an application example in the wine recognition dataset from the UCI Machine Learning data repository.",
keywords = "Granular entropy, Hybrid Knowledge, Knowledge granules, Knowledge reduction, Rough sets",
author = "Liu, \{Jin Fu\} and Yu, \{Da Ren\} and Hu, \{Qing Hua\} and Li, \{Xiao Dong\}",
year = "2004",
doi = "10.1109/ICMLC.2004.1382084",
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 = "1878--1883",
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",
}