ESKNN (version 1.0)

esknnProb: Train the ensemble of subset of k-nearest neighbours classifiers for estimation of class membership probabilty.

Description

This function selects a subset of optimal models from a set of m models, initially generated on bootstrap sample with a random feature subset from the training data, for class membership probability estimation. The values for the hyper parameters, for example subset size of the best models from the total initial m models, can be specified by the user otherwise the default values are considered.

Usage

esknnProb(xtrain, ytrain, k = NULL, q = NULL, m = NULL, ss = NULL)

Arguments

xtrain

A matrix or data frame of size n x d dimension where n is the number of traing observation and d is the number of features.

ytrain

A vector of class labels for the training data. Class labels should be factor of two levels (0,1) represented by variable Class in the data.

k

Number of nearest neighbours to be considered, when NULL then the default is set tok=3.

q

Percent of models to be selected from the initial set m.

m

Number of models to be generated in the first stage, when NULL the default is m=501.

ss

Feature subset size to be selected from d features for each bootstrap sample, when NULL the default is (number of features)/3.

Value

trainfinal

List of the extracted opimal models.

fsfinal

List of the features used in each selected models.

References

Gul, A., Perperoglou, A., Khan, Z., Mahmoud, O.,Miftahuddin, M., Adler, W. and Lausen, B.(2014),Ensemble of Subset of kNN Classifiers, Journal name to appear.

See Also

Predict.esknnProb

Examples

# Load the data

  data(sonar)
  data <- sonar

# Divide the data into testing and training

  Class <- data[,names(data)=="Class"]
  data$Class<-as.factor(as.numeric(Class)-1)
  train <- data[sample(1:nrow(data),0.7*nrow(data)),]
  test <- data[-(sample(1:nrow(data),0.7*nrow(data))),]
  ytrain<-train[,names(train)=="Class"]
  xtrain<-train[,names(train)!="Class"]
  xtest<-test[,names(test)!="Class"]
  ytest <- test[,names(test)=="Class"]

# Trian esknnProb on training data

  model<-esknnProb(xtrain, ytrain,k=NULL)

# Predict on test data

  resProb<-Predict.esknnProb(model,xtest,ytest,k=NULL)

## Returning Objects

  resProb$PredProb
  resProb$BrierScore