Functions for classification and group membership probability estimation are given. The issue of non-informative features in the data is addressed by utilizing the ensemble method. A few optimal models are selected in the ensemble from an initially large set of base k-nearest neighbours (KNN) models, generated on subset of features from the training data. A two stage assessment is applied in selection of optimal models for the ensemble in the training function. The prediction functions for classification and class membership probability estimation returns class outcomes and class membership probability estimates for the test data. The package includes measure of classification error and brier score, for classification and probability estimation tasks respectively.
|Author:||Asma Gul, Aris Perperoglou, Zardad Khan, Osama Mahmoud, Werner Adler, Miftahuddin Miftahuddin, and Berthold Lausen|
|Maintainer:||Asma Gul <agul at essex.ac.uk>|
|License:||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]|
|CRAN checks:||ESKNN results|
|Windows binaries:||r-devel: ESKNN_1.0.zip, r-release: ESKNN_1.0.zip, r-oldrel: ESKNN_1.0.zip|
|OS X El Capitan binaries:||r-release: ESKNN_1.0.tgz|
|OS X Mavericks binaries:||r-oldrel: ESKNN_1.0.tgz|
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