ExNRuleEnsemble: A k Nearest Neibour Ensemble Based on Extended Neighbourhood Rule

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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