\documentclass[a4paper]{article}
\usepackage[round]{natbib}

\usepackage{hyperref}
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%\VignetteIndexEntry{Classification of UCI Machine Learning Datasets (without Results)}
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\hypersetup{%
  pdftitle = {Classification of UCI Machine Learning Datasets},
  pdfsubject = {package vignette},
  pdfauthor = {Zhu Wang},
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\author{Zhu Wang \\
  UT Health San Antonio\\
  wangz1@uthscsa.edu}
\title{Classification of UCI Machine Learning Datasets}
\begin{document}
\setkeys{Gin}{width=0.6\textwidth, keepaspectratio}
\date{}
\maketitle

This document presents benchmark data analysis similar to \cite{wang2011multi}  using R package \textbf{bst}.
We compare the multi-class HingeBoost using three different algorithms for four benchmark data sets available from the UCI repository of machine learning data. We utilized regression
trees as base learners in the HingeBoost. The number of terminal nodes is related to the depth of the
tree, and the degree of interactions. To illustrate, we present the results for maximum tree depth 6. 

<<echo=FALSE,results='hide'>>=
library("knitr")
options(prompt = "R> ", continue = " ", width = 70, digits =4, useFancyQuotes = FALSE)
opts_chunk$set(tidy=TRUE, highlight=FALSE, background="white")
@
\section{Image segmentation data}
<<results='hide', message=FALSE>>=
library("bst")
@
<<echo=TRUE, results='hide', eval=F>>=
tmp <- "https://archive.ics.uci.edu/ml/machine-learning-databases/image/"
dat1 <- "segmentation.data"
dat1 <- read.delim(paste(tmp, dat1, sep=""), sep=",", header=FALSE, skip=5)
dat2 <- "segmentation.test"
dat2 <- read.delim(paste(tmp, dat2, sep=""), sep=",", header=FALSE, skip=5)
dat1[,1] <- as.numeric(dat1[,1])
dat2[,1] <- as.numeric(dat2[,1]) 
m <- 500
dat.m1 <- mbst(x=dat1[,-1], y=dat1[,1], ctrl = bst_control(mstop=m), control.tree=list(maxdepth=6), family = "hinge", learner = "tree")
err.te1 <- predict(dat.m1, newdata=dat2[,-1], newy=dat2[,1], mstop=m, type="error")
dat.m2 <- mbst(x=dat1[,-1], y=dat1[,1], ctrl = bst_control(mstop=m), control.tree=list(maxdepth=6), family = "hinge2", learner = "tree")
err.te2 <- predict(dat.m2, newdata=dat2[,-1], newy=dat2[,1], mstop=m, type="error")
dat.m3 <- mhingebst(x=dat1[,-1], y=dat1[,1], ctrl = bst_control(mstop=m), control.tree=list(maxdepth=6), family = "hinge", learner = "tree")
err.te3 <- predict(dat.m3, newdata=dat2[,-1], newy=dat2[,1], mstop=m, type="error")
plot(err.te1, type="l", xlab="Iteration", ylab="Test Error", ylim=c(0.05, 0.12))
points(err.te2,  type="l", lty="dashed", col="blue")
points(err.te3,  type="l", lty="dotted", col="red")
legend("topright", c("mbst_hinge", "mbst_hinge2", "mhingebst"), lty=c("solid", "dashed", "dotted"), col=c("black", "blue", "red"))
@

\section{Thyroid disease classification}
<<echo=TRUE, results='hide', eval=F>>=
tmp <- "http://archive.ics.uci.edu/ml/machine-learning-databases/thyroid-disease/"
dat1 <- "ann-train.data"
dat1 <- read.table(paste(tmp, dat1, sep=""))
dat2 <- "ann-test.data"
dat2 <- read.table(paste(tmp, dat2, sep=""))
m <- 400
dat.m1 <- mbst(x=dat1[,-22], y=dat1[,22], ctrl = bst_control(mstop=m), control.tree=list(maxdepth=6), family = "hinge", learner = "tree")
err.te1 <- predict(dat.m1, newdata=dat2[,-22], newy=dat2[,22], mstop=m, type="error")
dat.m2 <- mbst(x=dat1[,-22], y=dat1[,22], ctrl = bst_control(mstop=m), control.tree=list(maxdepth=6), family = "hinge2", learner = "tree")
err.te2 <- predict(dat.m2, newdata=dat2[,-22], newy=dat2[,22], mstop=m, type="error")
dat.m3 <- mhingebst(x=dat1[,-22], y=dat1[,22], ctrl = bst_control(mstop=m), control.tree=list(maxdepth=6), family = "hinge", learner = "tree")
err.te3 <- predict(dat.m3, newdata=dat2[,-22], newy=dat2[,22], mstop=m, type="error")
plot(err.te1, type="l", xlab="Iteration", ylab="Test Error", ylim=c(0.005, 0.01))
points(err.te2,  type="l", lty="dashed", col="blue")
points(err.te3,  type="l", lty="dotted", col="red")
legend("topright", c("mbst_hinge", "mbst_hinge2", "mhingebst"), lty=c("solid", "dashed", "dotted"), col=c("black", "blue", "red"))
@
\section{Satellite image classification}
<<echo=TRUE, results='hide', eval=F>>=
tmp <- "http://archive.ics.uci.edu/ml/machine-learning-databases/statlog/satimage/"
train <- "sat.trn"
train <- read.table(paste(tmp, train, sep=""))
test <- "sat.tst"
test <- read.table(paste(tmp, test, sep=""))
train[,37] <- as.numeric(as.factor(train[,37]))
test[,37] <- as.numeric(as.factor(test[,37]))
p <- 37
colnames(train)[1:(p-1)] <- paste("x", 1:(p-1), sep = "")
colnames(test)[1:(p-1)] <- paste("x", 1:(p-1), sep = "")
m <- 600
dat.m1 <- mbst(x=train[,-37], y=train[,37], ctrl = bst_control(mstop=m), control.tree=list(fixed.depth=FALSE, maxdepth=6, n.term.node=6), family = "hinge", learner = "tree")
err.te1 <- predict(dat.m1, newdata=test[,-37], newy=test[,37], mstop=m, type="error")
dat.m2 <- mbst(x=train[,-37], y=train[,37], ctrl = bst_control(mstop=m), control.tree=list(fixed.depth=FALSE, maxdepth=6, n.term.node=6), family = "hinge2", learner = "tree")
err.te2 <- predict(dat.m2, newdata=test[,-37], newy=test[,37], mstop=m, type="error")
dat.m3 <- mhingebst(x=train[,-37], y=train[,37], ctrl = bst_control(mstop=m), control.tree=list(fixed.depth=FALSE, maxdepth=6, n.term.node=6), family = "hinge", learner = "tree")
err.te3 <- predict(dat.m3, newdata=test[,-37], newy=test[,37], mstop=m, type="error")
plot(err.te1, type="l", xlab="Iteration", ylab="Test Error", ylim=c(0, 0.3))
points(err.te2,  type="l", lty="dashed", col="blue")
points(err.te3,  type="l", lty="dotted", col="red")
legend("topright", c("mbst_hinge", "mbst_hinge2", "mhingebst"), lty=c("solid", "dashed", "dotted"), col=c("black", "blue", "red"))
@
\section{Glass identification database}
<<echo=TRUE, results='hide', eval=F>>=
dat <- "https://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data"
dat <- read.delim(dat, sep=",", header=FALSE)[,-1]
### there is no class 4
table(dat[,10])
### recode class label
id <- dat[,10] > 3
dat[id, 10] <- dat[id,10] - 1
table(dat[,10])
p <- ncol(dat)
colnames(dat)[1:(p-1)] <- paste("x", 1:(p-1), sep = "")
set.seed(153)
### generate 10 balanced training data and test data, using 9 folds for training and one for test
allfolds <- balanced.folds(dat[,10], nfolds=10)
omit <- allfolds[[1]]
train <- dat[-omit,]
test <- dat[omit,]
m <- 200
dat.m1 <- mbst(x=train[,-p], y=train[,p], ctrl = bst_control(mstop=m), control.tree=list(maxdepth=6), family = "hinge", learner = "tree")
err.te1 <- predict(dat.m1, newdata=test[,-p], newy=test[,p], mstop=m, type="error")
dat.m2 <- mbst(x=train[,-p], y=train[,p], ctrl = bst_control(mstop=m), control.tree=list(maxdepth=6), family = "hinge2", learner = "tree")
err.te2 <- predict(dat.m2, newdata=test[,-p], newy=test[,p], mstop=m, type="error")
dat.m3 <- mhingebst(x=train[,-p], y=train[,p], ctrl = bst_control(mstop=m), control.tree=list(maxdepth=6), family = "hinge", learner = "tree")
err.te3 <- predict(dat.m3, newdata=test[,-p], newy=test[,p], mstop=m, type="error")
plot(err.te1, type="l", xlab="Iteration", ylab="Test Error")
points(err.te2,  type="l", lty="dashed", col="blue")
points(err.te3,  type="l", lty="dotted", col="red")
legend("topright", c("mbst_hinge", "mbst_hinge2", "mhingebst"), lty=c("solid", "dashed", "dotted"), col=c("black", "blue", "red"))
@
<<echo=FALSE, eval= FALSE>>=
### cross-validation
dat.cvm1 <- cv.mbst(x=dat[,-p], y=dat[,p], balance=TRUE, K=10, ctrl = bst_control(mstop=m), control.tree=list(maxdepth=6), family = "hinge", learner = "tree", type="error")
dat.cvm2 <- cv.mbst(x=dat[,-p], y=dat[,p], balance=TRUE, K=10, ctrl = bst_control(mstop=m), control.tree=list(maxdepth=6), family = "hinge2", learner = "tree", type="error")
dat.cvm3 <- cv.mhingebst(x=dat[,-p], y=dat[,p], balance=TRUE, K=10, ctrl = bst_control(mstop=m), control.tree=list(maxdepth=6), family = "hinge", learner = "tree", type="error")
plot(dat.cvm1$cv, type="l", xlab="Iteration", ylab="Test Error")
points(dat.cvm2$cv,  type="l", lty="dashed", col="blue")
points(dat.cvm3$cv,  type="l", lty="dotted", col="red")
legend("topright", c("mbst_hinge", "mbst_hinge2", "mhingebst"), lty=c("solid", "dashed", "dotted"), col=c("black", "blue", "red"))
@

\bibliographystyle{plainnat}
\bibliography{bst}
\end{document}

