It's a Super K-Nearest Neighbor(SKNN) classification method with using kernel density to describe weight of the distance between a training observation and the testing sample. Comparison of performance between SKNN and KNN shows that SKNN is significantly superior to KNN.
| Version: | 4.1.3 |
| Depends: | methods, stats |
| Published: | 2026-02-08 |
| DOI: | 10.32614/CRAN.package.SKNN |
| Author: | Yarong Yang [aut, cre], Nader Ebrahimi [ctb], Yoram Rubin [ctb], Jacob Zhang [ctb] |
| Maintainer: | Yarong Yang <Yi.YA_yaya at hotmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | SKNN results |
| Reference manual: | SKNN.html , SKNN.pdf |
| Package source: | SKNN_4.1.3.tar.gz |
| Windows binaries: | r-devel: SKNN_4.1.3.zip, r-release: SKNN_4.1.3.zip, r-oldrel: SKNN_4.1.3.zip |
| macOS binaries: | r-release (arm64): SKNN_4.1.3.tgz, r-oldrel (arm64): SKNN_4.1.3.tgz, r-release (x86_64): SKNN_4.1.3.tgz, r-oldrel (x86_64): SKNN_4.1.3.tgz |
| Old sources: | SKNN archive |
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