dknn.classify.trained {ddalpha}

Depth-Based kNN

Description

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

Usage

dknn.classify.trained(objects, dknn)

Arguments

objects

Matrix containing objects to be classified; each row is one d-dimensional object.

dknn

Dknn-classifier (obtained by dknn.train).

Value

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

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 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)