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The extended neighbourhood rule for the k nearest neighbour ensemble where the neighbours are determined in k steps. Starting from the first nearest observation of the test point, the algorithm identifies a single observation that is closest to the observation at the previous step. At each base learner in the ensemble, this search is extended to k steps on a random bootstrap sample with a random subset of features selected from the feature space. The final predicted class of the test point is determined by using a majority vote in the predicted classes given by all base models. Amjad Ali, Muhammad Hamraz, Naz Gul, Dost Muhammad Khan, Saeed Aldahmani, Zardad Khan (2022) <doi:10.48550/arXiv.2205.15111>.
| Version: | 0.1.1 |
| Depends: | R (≥ 2.10) |
| Imports: | FNN |
| Published: | 2022-12-19 |
| Author: | Amjad Ali [aut, cre, cph], Muhammad Hamraz [aut], Saeed Aldahmani [aut], Zardad Khan [aut] |
| Maintainer: | Amjad Ali <Amjad.ali at awkum.edu.pk> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| CRAN checks: | ExNRuleEnsemble results |
| Reference manual: | ExNRuleEnsemble.pdf |
| Package source: | ExNRuleEnsemble_0.1.1.tar.gz |
| Windows binaries: | r-devel: ExNRuleEnsemble_0.1.1.zip, r-release: ExNRuleEnsemble_0.1.1.zip, r-oldrel: ExNRuleEnsemble_0.1.1.zip |
| macOS binaries: | r-release (arm64): ExNRuleEnsemble_0.1.1.tgz, r-oldrel (arm64): ExNRuleEnsemble_0.1.1.tgz, r-release (x86_64): ExNRuleEnsemble_0.1.1.tgz, r-oldrel (x86_64): ExNRuleEnsemble_0.1.1.tgz |
| Old sources: | ExNRuleEnsemble archive |
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