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Fuzzy KNN Method With Adaptive Nearest Neighbors.
- Source :
- IEEE Transactions on Cybernetics; Jun2022, Vol. 52 Issue 6, p5380-5393, 14p
- Publication Year :
- 2022
-
Abstract
- Due to its strong performance in handling uncertain and ambiguous data, the fuzzy ${k}$ -nearest-neighbor method (FKNN) has realized substantial success in a wide variety of applications. However, its classification performance would be heavily deteriorated if the number k of nearest neighbors was unsuitably fixed for each testing sample. This study examines the feasibility of using only one fixed k value for FKNN on each testing sample. A novel FKNN-based classification method, namely, fuzzy KNN method with adaptive nearest neighbors (A-FKNN), is devised for learning a distinct optimal k value for each testing sample. In the training stage, after applying a sparse representation method on all training samples for reconstruction, A-FKNN learns the optimal ${k}$ value for each training sample and builds a decision tree (namely, A-FKNN tree) from all training samples with new labels (the learned optimal ${k}$ values instead of the original labels), in which each leaf node stores the corresponding optimal ${k}$ value. In the testing stage, A-FKNN identifies the optimal ${k}$ value for each testing sample by searching the A-FKNN tree and runs FKNN with the optimal ${k}$ value for each testing sample. Moreover, a fast version of A-FKNN, namely, FA-FKNN, is designed by building the FA-FKNN decision tree, which stores the optimal k value with only a subset of training samples in each leaf node. Experimental results on 32 UCI datasets demonstrate that both A-FKNN and FA-FKNN outperform the compared methods in terms of classification accuracy, and FA-FKNN has a shorter running time. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21682267
- Volume :
- 52
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Cybernetics
- Publication Type :
- Academic Journal
- Accession number :
- 157551622
- Full Text :
- https://doi.org/10.1109/TCYB.2020.3031610