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.
Note This package is still very experimental and under active development. The function interface is subject to change without guarantee of backward compatibility.
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)
packageVersion("abclass")## [1] '0.5.0.9050'
## toy examples for demonstration purpose
## reference: example 1 in Zhang and Liu (2014)
ntrain <- 400 # size of training set
ntest <- 10000 # size of testing set
p0 <- 5 # number of actual predictors
p1 <- 45 # number of random predictors
k <- 5 # number of categories
set.seed(1)
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]
### logistic deviance loss with elastic-net penalty
model1 <- cv.abclass(train_x, train_y, nlambda = 100, nfolds = 5,
loss = "logistic", grouped = FALSE)
pred1 <- predict(model1, test_x)
table(test_y, pred1)## pred1
## test_y label_1 label_2 label_3 label_4 label_5
## label_1 1347 0 2 654 0
## label_2 2 1856 2 0 108
## label_3 3 6 1763 0 180
## label_4 0 8 0 1922 102
## label_5 0 68 37 1 1939
mean(test_y == pred1) # accuracy## [1] 0.8827
### with groupwise lasso
model2 <- cv.abclass(train_x, train_y, nlambda = 100, nfolds = 5,
loss = "logistic", grouped = TRUE)
pred2 <- predict(model2, test_x)
table(test_y, pred2)## pred2
## test_y label_1 label_2 label_3 label_4 label_5
## label_1 1993 1 2 3 4
## label_2 0 1780 0 0 188
## label_3 4 2 1368 0 578
## label_4 10 28 0 1964 30
## label_5 0 10 3 0 2032
mean(test_y == pred2) # accuracy## [1] 0.9137
## tuning by ET-Lasso instead of cross-validation
model3 <- et.abclass(train_x, train_y, nlambda = 100,
loss = "logistic", grouped = TRUE)
pred3 <- predict(model3, test_x)
table(test_y, pred3)## pred3
## test_y label_1 label_2 label_3 label_4 label_5
## label_1 1991 1 5 5 1
## label_2 0 1843 0 0 125
## label_3 3 5 1676 0 268
## label_4 6 12 0 1999 15
## label_5 0 17 12 0 2016
mean(test_y == pred3) # accuracy## [1] 0.9525
- Zhang, C., & Liu, Y. (2014). Multicategory Angle-Based Large-Margin Classification. Biometrika, 101(3), 625–640.
- Liu, Y., Zhang, H. H., & Wu, Y. (2011). Hard or soft classification? large-margin unified machines. Journal of the American Statistical Association, 106(493), 166–177.
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