bst (version 0.3-2)

mhinge: Multi-class HingeBoost

Description

Multi-class algorithm with one-vs-all binary HingeBoost which optimizes the hinge loss functions with componentwise linear, smoothing splines, tree models as base learners.

Usage

mhinge(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 'mhinge':
print(x, ...)

Arguments

xtr

training data containing the predictor variables.

ytr

vector of training data responses. ytr must be in {1,2,...,k}.

xte

test data containing the predictor variables.

yte

vector of test data responses. yte must be in {1,2,...,k}.

cost

default is NULL for equal cost; otherwise a numeric vector indicating price to pay for false positive, 0 < cost < 1; price of false negative is 1-cost.

nu

a small number (between 0 and 1) defining the step size or shrinkage parameter.

learner

a character specifying the component-wise base learner to be used: ls linear models, sm smoothing splines, tree regression trees.

maxdepth

tree depth used in learner=tree

m1

number of boosting iteration

twinboost

logical: twin boosting?

m2

number of twin boosting iteration

x

class of mhinge.

...

additional arguments.

Value

• An object of class mhinge with print method being available.

Details

For a C-class 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 multi-class HingeBoost classifier is fitted using a boosting algorithm based on one-against component-wise base learners for +1/-1 responses, with possible cost-sensitive hinge loss function.

References

Zhu Wang (2011), HingeBoost: ROC-Based Boost for Classification and Variable Selection. The International Journal of Biostatistics, 7(1), Article 13.

Zhu Wang (2011), Multi-class HingeBoost: Method and Application to the Classification of Cancer Types Using Gene Expression Data. Manuscript.

See Also

bst for HingeBoost binary classification. Furthermore see cv.bst for stopping iteration selection by cross-validation, and bst_control for control parameters.

Examples

dat1 <- read.table("http://archive.ics.uci.edu/ml/machine-learning-databases/
thyroid-disease/ann-train.data")
dat2 <- read.table("http://archive.ics.uci.edu/ml/machine-learning-databases/
thyroid-disease/ann-test.data")
res <- mhinge(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 <- mhinge(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)