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
|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|
Last month downloads
|License||GPL (>= 2)|
|Packaged||2015-09-13 05:41:37 UTC; Khan|
Include our badge in your README