The package abclass provides implementations of the multi-category angle-based classifiers (Zhang & Liu, 2014) with the large-margin unified machines (Liu, et al., 2011) for high-dimensional data.
Notice that the package is still experimental and under active development.
One can install the released version from CRAN.
install.packages("abclass")Alternatively, the version under development can be installed as follows:
if (! require(remotes)) install.packages("remotes")
remotes::install_github("wenjie2wang/abclass", upgrade = "never")A toy example is as follows:
library(abclass)
set.seed(123)
## toy examples for demonstration purpose
## reference: example 1 in Zhang and Liu (2014)
ntrain <- 100 # size of training set
ntest <- 1000 # size of testing set
p0 <- 10 # number of actual predictors
p1 <- 100 # number of random predictors
k <- 5 # number of categories
n <- ntrain + ntest; p <- p0 + p1
train_idx <- seq_len(ntrain)
y <- sample(k, size = n, replace = TRUE) # response
mu <- matrix(rnorm(p0 * k), nrow = k, ncol = p0) # mean vector
## normalize the mean vector so that they are distributed on the unit circle
mu <- mu / apply(mu, 1, function(a) sqrt(sum(a ^ 2)))
x0 <- t(sapply(y, function(i) rnorm(p0, mean = mu[i, ], sd = 0.25)))
x1 <- matrix(rnorm(p1 * n, sd = 0.3), nrow = n, ncol = p1)
x <- cbind(x0, x1)
train_x <- x[train_idx, ]
test_x <- x[- train_idx, ]
y <- factor(paste0("label_", y))
train_y <- y[train_idx]
test_y <- y[- train_idx]
### regularization through elastic-net penalty
## logistic deviance loss
model1 <- abclass(train_x, train_y, nlambda = 100,
nfolds = 3, loss = "logistic")
pred1 <- predict(model1, test_x)
table(test_y, pred1)## pred1
## test_y label_1 label_2 label_3 label_4 label_5
## label_1 179 1 26 0 0
## label_2 1 200 1 1 0
## label_3 4 0 195 0 0
## label_4 0 2 4 183 3
## label_5 1 0 3 2 194mean(test_y == pred1) # accuracy## [1] 0.951## exponential loss approximating AdaBoost
model2 <- abclass(train_x, train_y, nlambda = 100,
nfolds = 3, loss = "boost")
pred2 <- predict(model2, test_x, s = "cv_1se")
table(test_y, pred2)## pred2
## test_y label_1 label_2 label_3 label_4 label_5
## label_1 184 0 22 0 0
## label_2 0 202 0 1 0
## label_3 18 1 176 3 1
## label_4 1 5 3 177 6
## label_5 1 0 0 1 198mean(test_y == pred2) # accuracy## [1] 0.937## hybrid hinge-boost loss
model3 <- abclass(train_x, train_y, nlambda = 100,
nfolds = 3, loss = "hinge-boost")
pred3 <- predict(model3, test_x)
table(test_y, pred3)## pred3
## test_y label_1 label_2 label_3 label_4 label_5
## label_1 179 1 26 0 0
## label_2 1 201 0 1 0
## label_3 5 0 194 0 0
## label_4 0 2 3 185 2
## label_5 1 0 2 2 195mean(test_y == pred3) # accuracy## [1] 0.954## large-margin unified loss
model4 <- abclass(train_x, train_y, nlambda = 100,
nfolds = 3, loss = "lum")
pred4 <- predict(model4, test_x)
table(test_y, pred4)## pred4
## test_y label_1 label_2 label_3 label_4 label_5
## label_1 179 1 26 0 0
## label_2 1 201 0 1 0
## label_3 4 0 194 0 1
## label_4 0 2 3 185 2
## label_5 1 0 1 0 198mean(test_y == pred4) # accuracy## [1] 0.957### variable selection via group lasso
## logistic deviance loss
model1 <- abclass(train_x, train_y, nlambda = 100, nfolds = 3,
grouped = TRUE, loss = "logistic")
pred1 <- predict(model1, test_x, s = "cv_1se")
table(test_y, pred1)## pred1
## test_y label_1 label_2 label_3 label_4 label_5
## label_1 173 1 32 0 0
## label_2 2 197 3 1 0
## label_3 1 1 197 0 0
## label_4 0 2 8 180 2
## label_5 2 0 5 3 190mean(test_y == pred1) # accuracy## [1] 0.937## exponential loss approximating AdaBoost
model2 <- abclass(train_x, train_y, nlambda = 100, nfolds = 3,
grouped = TRUE, loss = "boost")
pred2 <- predict(model2, test_x, s = "cv_1se")
table(test_y, pred2)## pred2
## test_y label_1 label_2 label_3 label_4 label_5
## label_1 189 0 17 0 0
## label_2 1 202 0 0 0
## label_3 11 0 187 0 1
## label_4 0 1 2 181 8
## label_5 1 0 0 0 199mean(test_y == pred2) # accuracy## [1] 0.958## hybrid hinge-boost loss
model3 <- abclass(train_x, train_y, nlambda = 100, nfolds = 3,
grouped = TRUE, loss = "hinge-boost")
pred3 <- predict(model3, test_x)
table(test_y, pred3)## pred3
## test_y label_1 label_2 label_3 label_4 label_5
## label_1 174 1 31 0 0
## label_2 0 202 0 1 0
## label_3 10 0 188 0 1
## label_4 0 3 7 181 1
## label_5 1 0 1 3 195mean(test_y == pred3) # accuracy## [1] 0.94## large-margin unified loss
model4 <- abclass(train_x, train_y, nlambda = 100, nfolds = 3,
grouped = TRUE, loss = "lum")
pred4 <- predict(model4, test_x)
table(test_y, pred4)## pred4
## test_y label_1 label_2 label_3 label_4 label_5
## label_1 181 1 24 0 0
## label_2 0 202 0 1 0
## label_3 5 0 193 0 1
## label_4 0 2 5 183 2
## label_5 1 0 1 0 198mean(test_y == pred4) # accuracy## [1] 0.957GNU General Public License (≥ 3)
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