Uncertainty and Equivalence Relation Analysis for Hesitant Fuzzy-Rough Sets and Their Applications in Classification
Author | Zhang, Haiqing |
Author | Li, Daiwei |
Author | Wang, Tao |
Author | Li, Tianrui |
Author | Yu, Xi |
Author | Bouras, Abdelaziz |
Available date | 2023-04-09T08:34:51Z |
Publication Date | 2019 |
Publication Name | Computing in Science and Engineering |
Resource | Scopus |
Abstract | The fusion of hesitant fuzzy set (HFS) and fuzzy-rough set (FRS) is explored and applied into the task of classification due to its capability of conveying hesitant and uncertainty information. In this paper, on the basis of studying the equivalence relations between hesitant fuzzy elements and HFS operation updating, the target instances are classified by employing the lower and upper approximations in hesitant FRS theory. Extensive performance analysis has been conducted including classification accuracy results, execution time, and the impact of k parameter to evaluate the proposed hesitant fuzzy-rough nearest-neighbor (HFRNN) algorithm. The experimental analysis has shown that the proposed HFRNN algorithm significantly outperforms current leading algorithms in terms of fuzzy-rough nearest-neighbor, vaguely quantified rough sets, similarity nearest-neighbor, and aggregated-similarity nearest-neighbor. 1999-2011 IEEE. |
Sponsor | This work was supported in part by the National Natural Science Foundation of China under Grant 61602064, in part by Science and Technology Agency Project of Sichuan Province under Grant 2017HH0088, in part by the Fundamental Research Funds for the Central Universities under Grant 2682015QM02, and in part by Scientific Research Foundation of CUIT under Grant KYTZ201615. |
Language | en |
Publisher | IEEE Computer Society |
Subject | classification equivalence relation fuzzy-rough sets hesitant fuzzy rough nearest neighbor hesitant fuzzy set |
Type | Article |
Pagination | 26-39 |
Issue Number | 6 |
Volume Number | 21 |
Files in this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |
This item appears in the following Collection(s)
-
Computer Science & Engineering [2283 items ]