Depth-Based kNN
The implementation of the affine-invariant depth-based kNN of Paindaveine and Van Bever (2015).
dknn.classify(objects, data, k, depth = "halfspace", seed = 0)
objects |
Matrix containing objects to be classified; each row is one d-dimensional object. |
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). |
k |
the number of neighbours |
depth |
Character string determining which depth notion to use; the default value is |
seed |
the random seed. The default value |
List containing class labels, or character string "Ignored" for the outsiders if "Ignore" was specified as the outsider treating method.
Paindaveine, D. and Van Bever, G. (2015). Nonparametrically consistent depth-based classifiers. Bernoulli 21 62–82.
dknn.train to train the Dknn-classifier.
dknn.classify.trained to classify with the Dknn-classifier.
ddalpha.train to train the DDα-classifier.
ddalpha.getErrorRateCV and ddalpha.getErrorRatePart to get error rate of the Dknn-classifier on particular data (set separator = "Dknn").
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.