Journal
COMPUTING IN SCIENCE & ENGINEERING
Volume 21, Issue 6, Pages 26-39Publisher
IEEE COMPUTER SOC
DOI: 10.1109/MCSE.2018.110150747
Keywords
Classification algorithms; Approximation algorithms; Fuzzy sets; Algorithm design and analysis; Training; Uncertainty; hesitant fuzzy set; fuzzy-rough sets; hesitant fuzzy rough nearest neighbor; classification; equivalence relation
Funding
- National Natural Science Foundation of China [61602064]
- Science and Technology Agency Project of Sichuan Province [2017HH0088]
- Fundamental Research Funds for the Central Universities [2682015QM02]
- Scientific Research Foundation of CUIT [KYTZ201615]
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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.
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