ddalpha (version 1.3.16)

dknn.classify: Depth-Based kNN

Description

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

Usage

dknn.classify(objects, data, k, depth = "halfspace", seed = 0)

Value

List containing class labels, or character string "Ignored" for the outsiders if "Ignore" was specified as the outsider treating method.

Arguments

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 "halfspace". Currently the method supports the following depths: "halfspace", "Mahalanobis", "simplicial".

seed

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

References

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

See Also

dknn.train to train the Dknn-classifier.

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)