Fuzziness-Preserving attribute reduction from hybrid data

Authors:
Wei Wei;Jiye Liang;Yuhua Qian;Junhong Wang
Affiliations:
Key Laboratory of Computational Intelligence and Chinese Information Processing, of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China;Key Laboratory of Computational Intelligence and Chinese Information Processing, of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China;Key Laboratory of Computational Intelligence and Chinese Information Processing, of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China;Key Laboratory of Computational Intelligence and Chinese Information Processing, of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

Citing 9
Cited 0

Uncertainly measures of rough set prediction

Artificial Intelligence
Rough set approach to incomplete information systems

Information Sciences: an International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation

Pattern Recognition
Measures for evaluating the decision performance of a decision table in rough set theory

Information Sciences: an International Journal
The investigation of the Bayesian rough set model

International Journal of Approximate Reasoning
Approximation reduction in inconsistent incomplete decision tables

Knowledge-Based Systems
Fuzzy probabilistic approximation spaces and their information measures

IEEE Transactions on Fuzzy Systems
Input feature selection for classification problems

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we devote to present a fuzziness-preserving attribute reduction in fuzzy rough set framework. Through constructing the membership function of an object, we first introduce a fuzzy measure to assess the fuzziness of a fuzzy rough set and a fuzzy rough decision, which underlies a foundation for attribute reduction algorithm. Then, we derive an attribute significance measure based on the proposed fuzzy measure and design a forward greedy algorithm (ARBF) for attribute reduction from hybrid. Numerical experiments show the validity of the proposed algorithm from search strategy and heuristic function in the meaning of fuzziness-preserving.