mhingeova {bst}  R Documentation 
Multiclass algorithm with onevsall binary HingeBoost which optimizes the hinge loss functions with componentwise linear, smoothing splines, tree models as base learners.
mhingeova(xtr, ytr, xte=NULL, yte=NULL, cost = NULL, nu=0.1, learner=c("tree", "ls", "sm"), maxdepth=1, m1=200, twinboost = FALSE, m2=200) ## S3 method for class 'mhingeova' print(x, ...)
xtr 
training data containing the predictor variables. 
ytr 
vector of training data responses. 
xte 
test data containing the predictor variables. 
yte 
vector of test data responses. 
cost 
default is NULL for equal cost; otherwise a numeric vector indicating price to pay for false positive, 0 < 
nu 
a small number (between 0 and 1) defining the step size or shrinkage parameter. 
learner 
a character specifying the componentwise base learner to be used:

maxdepth 
tree depth used in 
m1 
number of boosting iteration 
twinboost 
logical: twin boosting? 
m2 
number of twin boosting iteration 
x 
class of 
... 
additional arguments. 
For a Cclass problem (C > 2), each class is separately compared against all other classes with HingeBoost, and C functions are estimated to represent confidence for each class. The classification rule is to assign the class with the largest estimate. A linear or nonlinear multiclass HingeBoost classifier is fitted using a boosting algorithm based on oneagainst componentwise base learners for +1/1 responses, with possible costsensitive hinge loss function.
An object of class mhingeova
with print
method being available.
Zhu Wang
Zhu Wang (2011), HingeBoost: ROCBased Boost for Classification and Variable Selection. The International Journal of Biostatistics, 7(1), Article 13.
Zhu Wang (2012), Multiclass HingeBoost: Method and Application to the Classification of Cancer Types Using Gene Expression Data. Methods of Information in Medicine, 51(2), 162–7.
bst
for HingeBoost binary classification. Furthermore see cv.bst
for stopping iteration selection by crossvalidation, and bst_control
for control parameters.
## Not run: dat1 < read.table("http://archive.ics.uci.edu/ml/machinelearningdatabases/ thyroiddisease/anntrain.data") dat2 < read.table("http://archive.ics.uci.edu/ml/machinelearningdatabases/ thyroiddisease/anntest.data") res < mhingeova(xtr=dat1[,22], ytr=dat1[,22], xte=dat2[,22], yte=dat2[,22], cost=c(2/3, 0.5, 0.5), nu=0.5, learner="ls", m1=100, K=5, cv1=FALSE, twinboost=TRUE, m2= 200, cv2=FALSE) res < mhingeova(xtr=dat1[,22], ytr=dat1[,22], xte=dat2[,22], yte=dat2[,22], cost=c(2/3, 0.5, 0.5), nu=0.5, learner="ls", m1=100, K=5, cv1=FALSE, twinboost=TRUE, m2= 200, cv2=TRUE) ## End(Not run)