ESKNN v1.0

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

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.

Functions in ESKNN

Name Description
esknnClass Train ensemble of subset of k-nearest neighbours classifiers for classification
Predict.esknnClass Class predictions from ensemble of subset of k-nearest neighbours
sonar Sonar, Mines vs. Rocks.
hepatitis Hepatitis data set
esknnProb Train the ensemble of subset of k-nearest neighbours classifiers for estimation of class membership probabilty.
Predict.esknnProb Prediction function returning class membership probability estimates
ESkNN-package Ensemble of Subset of K-Nearest Neighbours Classifiers for Classification and Class Membership Probability Estimation
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Details

Type Package
Date 2015-09-13
LazyLoad yes
License GPL (>= 2)
NeedsCompilation no
Packaged 2015-09-13 05:41:37 UTC; Khan
Repository CRAN
Date/Publication 2015-09-13 09:22:47

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