bst (version 0.3-24)

mhingeova: 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

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 mhingeova
print(x, ...)

Value

An object of class mhingeova with print method being available.

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 mhingeova.

...

additional arguments.

Author

Zhu Wang

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 (2012), Multi-class HingeBoost: Method and Application to the Classification of Cancer Types Using Gene Expression Data. Methods of Information in Medicine, 51(2), 162--7.

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

if (FALSE) {
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 <- 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)
}