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.

Install the latest version of this package by entering the following in R:

`install.packages("ESKNN")`

Author | Asma Gul, Aris Perperoglou, Zardad Khan, Osama Mahmoud, Werner Adler, Miftahuddin Miftahuddin, and Berthold Lausen |

Date of publication | 2015-09-13 09:22:47 |

Maintainer | Asma Gul <agul@essex.ac.uk> |

License | GPL (>= 2) |

Version | 1.0 |

**esknnClass:** Train ensemble of subset of k-nearest neighbours classifiers...

**ESKNN-package:** Ensemble of Subset of K-Nearest Neighbours Classifiers for...

**esknnProb:** Train the ensemble of subset of k-nearest neighbours...

**hepatitis:** Hepatitis data set

**Predict.esknnClass:** Class predictions from ensemble of subset of k-nearest...

**Predict.esknnProb:** Prediction function returning class membership probability...

**sonar:** Sonar, Mines vs. Rocks.

NAMESPACE

data

data/hepatitis.rda

data/sonar.rda

R

R/Predict.esknnProb.R
R/esknnClass.R
R/Predict.esknnClass.R
R/esknnProb.R
MD5

DESCRIPTION

man

man/Predict.esknnProb.Rd
man/hepatitis.Rd
man/sonar.Rd
man/esknnProb.Rd
man/ESKNN-package.Rd
man/Predict.esknnClass.Rd
man/esknnClass.Rd
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