Movatterモバイル変換

abclass

The packageabclass provides implementations of themulti-category angle-based classifiers (Zhang & Liu, 2014) with thelarge-margin unified machines (Liu, et al., 2011) for high-dimensionaldata.

Note

This package is still very experimental and under active development.The function interface is subject to change without guarantee ofbackward compatibility.

Installation

One can install the released version fromCRAN.

install.packages("abclass")

Alternatively, the version under development can be installed asfollows:

if (!require(remotes))install.packages("remotes")remotes::install_github("wenjie2wang/abclass",upgrade ="never")

Getting Started

A toy example is as follows:

library(abclass)packageVersion("abclass")

## [1] '0.5.0'

## toy examples for demonstration purpose## reference: example 1 in Zhang and Liu (2014)ntrain<-400# size of training setntest<-10000# size of testing setp0<-5# number of actual predictorsp1<-45# number of random predictorsk<-5# number of categoriesset.seed(1)n<- ntrain+ ntest; p<- p0+ p1train_idx<-seq_len(ntrain)y<-sample(k,size = n,replace =TRUE)# responsemu<-matrix(rnorm(p0* k),nrow = k,ncol = p0)# mean vector## normalize the mean vector so that they are distributed on the unit circlemu<- 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 penaltymodel1<-cv.abclass(train_x, train_y,nlambda =100,nfolds =5,loss ="logistic",penalty ="lasso",alpha =0.9)pred1<-predict(model1, test_x)table(test_y, pred1)

##          pred1## test_y    label_1 label_2 label_3 label_4 label_5##   label_1    1704       0       3     296       0##   label_2       0    1862       0       0     106##   label_3       4      11    1739       0     198##   label_4       3      12       0    1947      70##   label_5       0      63      30       1    1951

mean(test_y== pred1)# accuracy

## [1] 0.9203

### with groupwise lassomodel2<-cv.abclass(train_x, train_y,nlambda =100,nfolds =5,loss ="logistic",penalty ="glasso")pred2<-predict(model2, test_x)table(test_y, pred2)

##          pred2## test_y    label_1 label_2 label_3 label_4 label_5##   label_1    1994       1       2       3       3##   label_2       0    1784       0       0     184##   label_3       4       2    1336       0     610##   label_4      12      27       0    1963      30##   label_5       0      10       2       0    2033

mean(test_y== pred2)# accuracy

## [1] 0.911

## tuning by ET-Lasso instead of cross-validationmodel3<-et.abclass(train_x, train_y,nlambda =100,loss ="logistic",penalty ="glasso")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    1842       0       0     126##   label_3       3       7    1643       0     299##   label_4       7      13       0    1997      15##   label_5       0      18      11       0    2016

mean(test_y== pred3)# accuracy

## [1] 0.9489

References

• Zhang, C., & Liu, Y. (2014). Multicategory Angle-BasedLarge-Margin Classification.Biometrika, 101(3), 625–640.
• Liu, Y., Zhang, H. H., & Wu, Y. (2011). Hard or softclassification? large-margin unified machines.Journal of theAmerican Statistical Association, 106(493), 166–177.

License

GNU General PublicLicense (≥ 3)

Copyright holder: Eli Lilly and Company