ESkNN-package

ESkNN

Functions for building an ensemble of optimal k-nearest neighbours (kNN) models for classification and class membership probability estimation are provided.  To address the issue of non-informative features in the data. A set of base kNN models is generated and a subset of models is selected for the ensemble based on the individual and combined performance of these models. Out-of-bag data and an independent training data set is used for the performance assessment of models individually and collectively. Class labels and class membership probability estimates are returned by the prediction functions. Other measures such as confusion matrix, classification error rate, and brier scores etc, are also returned by the functions.


 package

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.

ESkNN-package: Ensemble of Subset of K-Nearest Neighbours Classifiers for Classification and Class Membership Probability Estimation

Description

Arguments

Details

References