dknn.train {ddalpha}

Depth-Based kNN

Description

The implementation of the affine-invariant depht-based kNN of Paindaveine and Van Bever (2015).

Usage

dknn.train(data, kMax = -1, depth = "halfspace", seed = 0)

Arguments

data

Matrix containing training sample where each of n rows is one object of the training sample where first d entries are inputs and the last entry is output (class label).

kMax

the maximal value for the number of neighbours. If the value is set to -1, the default value is calculated as n/2, but at least 2, at most n-1.

depth

Character string determining which depth notion to use; the default value is "halfspace". Currently the method supports the following depths: "halfspace", "Mahalanobis", "simplicial".

seed

the random seed. The default value seed=0 makes no changes.

Value

The returned object contains technical information for classification, including the found optimal value k.

References

Paindaveine, D. and Van Bever, G. (2015). Nonparametrically consistent depth-based classifiers. Bernoulli 21 62–82.

See Also

dknn.classify and dknn.classify.trained to classify with the Dknn-classifier.

ddalpha.train to train the DD\alpha-classifier.

ddalpha.getErrorRateCV and ddalpha.getErrorRatePart to get error rate of the Dknn-classifier on particular data (set separator = "Dknn").

Examples

# Generate a bivariate normal location-shift classification task
# containing 200 training objects and 200 to test with
class1 <- mvrnorm(200, c(0,0), 
                  matrix(c(1,1,1,4), nrow = 2, ncol = 2, byrow = TRUE))
class2 <- mvrnorm(200, c(2,2), 
                  matrix(c(1,1,1,4), nrow = 2, ncol = 2, byrow = TRUE))
trainIndices <- c(1:100)
testIndices <- c(101:200)
propertyVars <- c(1:2)
classVar <- 3
trainData <- rbind(cbind(class1[trainIndices,], rep(1, 100)), 
                   cbind(class2[trainIndices,], rep(2, 100)))
testData <- rbind(cbind(class1[testIndices,], rep(1, 100)), 
                  cbind(class2[testIndices,], rep(2, 100)))
data <- list(train = trainData, test = testData)

# Train the classifier
# and get the classification error rate
cls <- dknn.train(data$train, kMax = 20, depth = "Mahalanobis")
cls$k
classes1 <- dknn.classify.trained(data$test[,propertyVars], cls)
cat("Classification error rate: ", 
    sum(unlist(classes1) != data$test[,classVar])/200)

# Classify the new data based on the old ones in one step
classes2 <- dknn.classify(data$test[,propertyVars], data$train, k = cls$k, depth = "Mahalanobis")
cat("Classification error rate: ", 
    sum(unlist(classes2) != data$test[,classVar])/200)