esknnClass: Train ensemble of subset of k-nearest neighbours classifiers for classification

Description

Constructing m, models and search for the optimal models for classification.

Usage

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

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

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

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

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.

Examples

Run this code

# Load the data

  data(hepatitis)
  data <- hepatitis

# Divide the data into testing and training parts

  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 esknnClass

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

# Predict on test data

  resClass<-Predict.esknnClass(model,xtest,ytest,k=NULL)
  
# Returning Objects are predicted class labels, confusion matrix and classification error

  resClass$PredClass
  resClass$ConfMatrix             
  resClass$ClassError