# ISL: Support Vector Machines

Yao Yao on October 8, 2014

## 0. Overview

• MMC 有一个缺点是 unfortunately cannot be applied to most data sets, since it requires that the classes be separable by a linear boundary.
• 接着我们搞出了一个 SVC (support vector classifier), an extension of the MMC that can be applied in a broader range of cases.
• SVM is a further extension of SVC in order to accommodate non-linear class boundaries.

SVM are intended for the binary classification setting in which there are two classes. 对于 multi class 的情况要用 one-vs-all。

## 1. Maximal Margin Classifier

### 1.1 What Is a Hyperplane?

In a $p$-dimensional space, a hyperplane is a flat affine subspace of dimension $p − 1$.

• affine: [ə’faɪn] indicates that the subspace need not pass through the origin
• 3 维空间的平面是 2 维的。hyperplane 只是把这种 1 维的差值关系扩展到多维空间而已。

The equation

$\begin{equation} \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \cdots + \beta_p X_p = 0 \tag{1.1} \label{eq1.1} \end{equation}$

defines a $p$-dimensional hyperplane.

If a point $X = (X_1, X_2, \cdots, X_p)^T$ in $p$-dimensional space (i.e. $X$ is a vector of length $p$) satisfies ($\ref{eq1.1}$), then $X$ lies on the hyperplane. 如果代入 ($\ref{eq1.1}$) 得到的是 > 0 或者 < 0，我们视为 $X$ 在 hyperplane 的两侧。

### 1.2 Classification Using a Separating Hyperplane

separating 是指 hyperplane 把 space 切成两半的情形。

Now suppose that we have a $n \times p$ data matrix $X$ that consists of $n$ training observations in $p$-dimensional space,

$\begin{equation} x_1 = \begin{pmatrix} x_{11} \newline \cdots \newline x_{1p} \end{pmatrix}, \cdots, x_n = \begin{pmatrix} x_{n1} \newline \cdots \newline x_{np} \end{pmatrix} \end{equation}$

and that these observations fall into two classes — that is, $y_1, \cdots, y_n \in \lbrace -1, 1 \rbrace$.

Suppose that it is possible to construct a hyperplane that separates the training observations perfectly according to their class labels.

• 对 $y_i = -1$ 的 $x_i$，代入 ($\ref{eq1.1}$) 得到的都是 < 0
• 对 $y_i = 1$ 的 $x_i$，代入 ($\ref{eq1.1}$) 得到的都是 > 0

Equivalently, a separating hyperplane has the property that

$\begin{equation} y_i (\beta_0 + \beta_1 x_{i1} + \beta_2 x_{i2} + \cdots + \beta_p x_{ip}) > 0 \tag{1.2} \end{equation}$

If a separating hyperplane exists, we can use it to construct a very natural classifier: a test observation is assigned a class depending on which side of the hyperplane it is located.

• If $f(x^{\ast})$ is far from 0, then this means that $x^{\ast}$ lies far from the hyperplane, and so we can be confident about our class assignment for $x^{\ast}$.
• On the other hand, if $f(x^{\ast})$ is close to zero, then $x^{\ast}$ is located near the hyperplane, and so we are less certain about the class assignment for $x^{\ast}$.

Not surprisingly, a classifier that is based on a separating hyperplane leads to a linear decision boundary.

### 1.3 The Maximal Margin Classifier

In general, if our data can be perfectly separated using a hyperplane, then there will in fact exist an infinite number of such hyperplanes. This is because a given separating hyperplane can usually be shifted a tiny bit up or down, or rotated, without coming into contact with any of the observations. In order to construct a classifier based upon a separating hyperplane, we must have a reasonable way to decide which one of the infinite possible separating hyperplanes to use.

A natural choice is the maximal margin hyperplane (a.k.a the optimal separating hyperplane). That is, we can compute the (perpendicular [ˌpɜ:pənˈdɪkjələ(r)]) distance from each training observation to a given separating hyperplane; the smallest such distance is the minimal distance from the observations to the hyperplane, and is known as the margin. The maximal margin hyperplane is the separating hyperplane for which the margin is largest — that is, it is the hyperplane that has the farthest minimum distance to the training observations.

• they are vectors in $p$-dimensional space
• they “support” the maximal margin hyperplane in the sense that if these points were moved slightly then the maximal margin hyperplane would move as well.

Interestingly, the maximal margin hyperplane depends directly on the support vectors, but not on the other observations: a movement to any of the other observations would not affect the separating hyperplane, provided that the observation’s movement does not cause it to cross the boundary set by the margin.

### 1.4 Construction of the Maximal Margin Classifier

P343。不难，解释得很清楚，一看就懂。公式太多我就不搬运了。

### 1.5 The Non-separable Case

In this case, we can extend the concept of a separating hyperplane in order to develop a hyperplane that almost separates the classes, using a so-called soft margin. The generalization of the maximal margin classifier to the non-separable case is known as the support vector classifier, discussed in the next chapter.

## 2. Support Vector Classifiers

### 2.1 Overview of the Support Vector Classifier

MMC 有两个缺点：

• MM Hyperplane 不一定存在
• 即使 MM Hyperplane 存在，它也存在着 sensitivity to individual observations
• 变动、增添或是移除某个点，对 Hyperplane 的位置可能造成 dramatic change
• 进一步导致 confidence 的变动
• Moreover, the fact that the maximal margin hyperplane is extremely sensitive to a change in a single observation suggests that it may have overfit the training data.

• Greater robustness to individual observations, and
• Better classification of most of the training observations.

That is, it could be worthwhile to misclassify a few training observations in order to do a better job in classifying the remaining observations.

The support vector classifier, sometimes called a soft margin classifier, does exactly this. Rather than seeking the largest possible margin, we instead allow some observations to be on the incorrect side of the margin (这里是把 margin 也看做是一个 hyperplane，有的点离 MM Hyperplane 更近，但是没有根据它来定 margin，也就说这些点在 margin 和 MM Hyperplane 的夹层内), or even the incorrect side of the hyperplane. (The margin is soft because it can be violated by some of the training observations.)

### 2.2 Details of the Support Vector Classifier

P346。在 P343 的公式基础上做了扩展。

\begin{align} y_i (\beta_0 + \beta_1 x_{i1} + \beta_2 x_{i2} + \cdots + \beta_p x_{ip}) \geq M(1- \epsilon_i) \newline \epsilon_i \geq 0, \, \sum_{i=1}^{n} \epsilon_i \leq C \end{align}
• $M$: the width of the margin
• $\epsilon_i$: slack (松弛的) variables
• If $\epsilon_i = 0$ then the i^th observation is on the correct side of the margin
• If $\epsilon_i > 0$ then the i^th observation is on the wrong side of the margin, and we say that the i^th observation has violated the margin
• If $\epsilon_i > 1$ then the i^th observation is on the wrong side of the hyperplane
• $C$ bounds the sum of the $\epsilon_i$’s, and so it determines the number and severity of the violations to the margin (and to the hyperplane) that we will tolerate. We can think of $C$ as a budget for the amount that the margin can be violated by the $n$ observations.
• If $C = 0$ then there is no budget for violations to the margin, and it must be the case that$\epsilon_1 = \cdots = \epsilon_n = 0$, in which case it simply amounts to the maximal margin hyperplane optimization problem.
• For $C > 0$ no more than $C$ observations can be on the wrong side of the hyperplane.
• As the budget $C$ increases, we become more tolerant of violations to the margin, and so the margin will widen.
• This amounts to fitting the data less hard and obtaining a classifier that is potentially more biased but may have lower variance. (Underfitting)
• As $C$ decreases, we become less tolerant of violations to the margin and so the margin narrows.
• This amounts to a classifier that is highly fit to the data, which may have low bias but high variance. (Overfitting)
• In practice, $C$ is treated as a tuning parameter that is generally chosen via CV.

• If $C$ is large, more support vectors.
• If $C$ is small, less support vectors.

The fact that the support vector classifier’s decision rule is based only on a potentially small subset of the training observations (the support vectors) means that it is quite robust to the behavior of observations that are far away from the hyperplane. This property is distinct from some of the other classification methods that we have seen in preceding chapters, such as linear discriminant analysis.

## 3. Support Vector Machines

### 3.1 Classification with Non-linear Decision Boundaries

It is not hard to see that there are many possible ways to enlarge the feature space, and that unless we are careful, we could end up with a huge number of features. Then computations would become unmanageable. The SVM, which we present next, allows us to enlarge the feature space used by the SVC in a way that leads to efficient computations.

### 3.2 The Support Vector Machine

The SVM is an extension of the SVC that results from enlarging the feature space in a specific way, using kernels.

When the SVC is combined with a non-linear kernel such as polynomial kernel, the resulting classifier is known as a SVM.

• linear kernel
• polynomial kernel

FIGURE 9.9. 值得一看，比较形象。

P354-355

P356-358

## 6. Lab: Support Vector Machines

• e1071
• LiblineaR, which is useful for very large linear problems.

### 6.1 Support Vector Classifier

The e1071 library contains implementations for a number of statistical learning methods. In particular, the svm() function can be used to fit a SVC when the argument kernel="linear" is used. This function uses a slightly different formulation from (9.14) and (9.25) for the SVC. A cost argument allows us to specify the cost of a violation to the margin.

• When the cost argument is small, then the margins will be wide and many support vectors will be on the margin or will violate the margin.
• When the cost argument is large, then the margins will be narrow and there will be few support vectors on the margin or violating the margin.

Here we demonstrate the use of this function on a two-dimensional example so that we can plot the resulting decision boundary. We begin by generating the observations, which belong to two classes.

> set.seed(1)
> x = matrix(rnorm(20*2), ncol=2)
> y = c(rep(-1,10), rep(1,10))
> x[y==1,] = x[y==1,] + 1 ## 我们自己造的数据


We begin by checking whether the classes are linearly separable.

> plot(x, col=(3-y)) ## col=2 是红色，col=4 是蓝色


They are not. Next, we fit the support vector classifier. Note that in order for the svm() function to perform classification (as opposed to SVM-based regression), we must encode the response as a factor variable. We now create a data frame with the response coded as a factor.

> dat = data.frame(x=x, y=as.factor(y))
> library(e1071)
> svmfit = svm(y~., data=dat, kernel="linear", cost=10, scale=FALSE) ## no feature scaling
> plot(svmfit, dat)


The decision boundary between the two classes is linear (because we used the argument kernel="linear"), though due to the way in which the plotting function is implemented in this library the decision boundary looks somewhat jagged in the plot.

• 红色点属于 purple region
• 黑色的点属于 light blue region
• 只有一个红色的点位于 light blue region，这是唯一的一个误判

O 和 X 并不是表示 classification 的对错的：

• X 表示这个点是 support vector
• O 表示这个点不是 support vector

We see here that there are 7 support vectors. We can determine their identities as follows:

> svmfit$index  1 2 5 7 14 16 17  We can obtain some basic information about the support vector classifier fit using the summary() command: > summary(svmfit)  It tells us, for instance, that a linear kernel was used with cost=10, and that there were 7 support vectors, 4 in one class and 3 in the other. What if we instead used a smaller value of the cost parameter? > svmfit = svm(y~., data=dat, kernel="linear", cost=0.1, scale=FALSE) > plot(svmfit, dat) > svmfit$index
 1 2 3 4 5 7 9 10 12 13 14 15 16 17 18 20


Now that a smaller value of the cost parameter is being used, we obtain a larger number of support vectors, because the margin is now wider. Unfortunately, the svm() function does not explicitly output the coefficients of the linear decision boundary obtained when the support vector classifier is fit, nor does it output the width of the margin.

The e1071 library includes a built-in function, tune(), to perform cross-validation. By default, tune() performs 10-fold cross-validation on a set of models of interest. In order to use this function, we pass in relevant information about the set of models that are under consideration. The following command indicates that we want to compare SVMs with a linear kernel, using a range of values of the cost parameter.

> set.seed(1)
> tune.out = tune(svm, y~., data=dat, kernel="linear", ranges=list(cost=c(0.001,0.01,0.1,1,5,10,100)))

> summary(tune.out)


We see that cost=0.1 results in the lowest cross-validation error rate. The tune() function stores the best model obtained, which can be accessed as follows:

> bestmod = tune.out$best.model > summary(bestmod)  To make predictions, we begin by generating a test data set.  xtest = matrix(rnorm(20*2), ncol=2) > ytest = sample(c(-1,1), 20, rep=TRUE) > xtest[ytest==1,] = xtest[ytest==1,] + 1 > testdat = data.frame(x=xtest, y=as.factor(ytest)) > ypred = predict(bestmod, testdat) > table(predict=ypred, truth=testdat$y)
truth
predict	    -1	1
-1  11  1
1   0  8
## 1 misclassification on test data


What if we had instead used cost=0.01?

> svmfit = svm(y~., data=dat, kernel="linear", cost=.01, scale=FALSE)
> ypred = predict(svmfit, testdat)
> table(predict=ypred, truth=testdat$y) truth predict -1 1 -1 11 2 1 0 7 ## however, 2 misclassification on test data  Now consider a situation in which the two classes are linearly separable. > x[y==1,] = x[y==1,]+0.5 > plot(x, col=(y+5)/2, pch=19) ## col=3 是绿色，col=2 是红色  Now the observations are just barely linearly separable. We fit the SVC and plot the resulting hyperplane, using a very large value of cost so that no observations are misclassified. > dat = data.frame(x=x, y=as.factor(y)) > svmfit = svm(y~., data=dat, kernel="linear", cost=1e5) > summary(svmfit) > plot(svmfit, dat)  No training errors were made and only three support vectors were used. However, we can see from the figure that the margin is very narrow (because the observations that are not support vectors, indicated as circles, are very close to the decision boundary). It seems likely that this model will perform poorly on test data. ### 6.2 Support Vector Machine In order to fit an SVMusing a non-linear kernel, we once again use the svm() function. However, now we use a different value of the parameter kernel. • To fit an SVM with a polynomial kernel, we use kernel="polynomial" and also a degree argument to specify a degree for the polynomial kernel (this is$ d $in (9.22)) • To fit an SVM with a radial kernel, we use kernel="radial" and also a gamma argument to specify a value of$ \gamma $for the radial basis kernel (9.24). We first generate some data with a non-linear class boundary, as follows: > set.seed(1) > x = matrix(rnorm(200*2), ncol=2) > x[1:100,] = x[1:100,]+2 > x[101:150,] = x[101:150,]-2 > y = c(rep(1,150), rep(2,50)) > dat = data.frame(x=x, y=as.factor(y))  Plotting the data makes it clear that the class boundary is indeed nonlinear: > plot(x, col=y) > train = sample(200,100) > svmfit = svm(y~., data=dat[train,], kernel="radial", gamma=1, cost=1) > plot(svmfit, dat[train,]) > summary(svmfit)  We can see from the figure that there are a fair number of training errors in this SVM fit. If we increase the value of cost, we can reduce the number of training errors. However, this comes at the price of a more irregular decision boundary that seems to be at risk of overfitting the data. > svmfit = svm(y~., data=dat[train,], kernel="radial", gamma=1, cost=1e5) > plot(svmfit, dat[train,]) ## a quite weird shape  We can perform cross-validation using tune() to select the best choice of$ \gamma $and cost for an SVM with a radial kernel: > set.seed(1) > tune.out = tune(svm, y~., data=dat[train,], kernel="radial", ranges=list(cost=c(0.1,1,10,100,1000), gamma=c(0.5,1,2,3,4))) > summary(tune.out)  Therefore, the best choice of parameters involves cost=1 and gamma=2. We can view the test set predictions for this model by applying the predict() function to the data. Notice that to do this we subset the dataframe dat using -train as an index set. > table(true=dat[-train,"y"], pred=predict(tune.out$best.model, newx=dat[-train,]))


39% of test observations are misclassified by this SVM.

### 6.3 ROC Curves

The ROCR package can be used to produce ROC curves. We first write a short function to plot an ROC curve given a vector containing a numerical score for each observation, pred, and a vector containing the class label for each observation, truth.

> library(ROCR)
> rocplot = function(pred, truth, ...) {
+ 	predob = prediction(pred, truth)
+ 	perf = performance(predob, "tpr", "fpr")
+ 	plot(perf, ...)
+ }


SVMs and support vector classifiers output class labels for each observation. However, it is also possible to obtain fitted values for each observation, which are the numerical scores used to obtain the class labels (也就是 $f(x^*)$，看是 > 0 还是 < 0 的那个值). In order to obtain the fitted values for a given SVM model fit, we use decision.values=TRUE when fitting svm(). Then the predict() function will output the fitted values.

> svmfit.opt = svm(y~., data=dat[train,], kernel="radial", gamma=2, cost=1, decision.values=T)
> fitted = attributes(predict(svmfit.opt, dat[train,], decision.values =TRUE))$decision.values  Now we can produce the ROC plot. > par(mfrow=c(1,2)) > rocplot(fitted, dat[train,"y"], main="Training Data")  SVM appears to be producing accurate predictions. By increasing$ \gamma $we can produce a more flexible fit and generate further improvements in accuracy. > svmfit.flex = svm(y~., data=dat[train,], kernel="radial", gamma=50, cost=1, decision.values=T) > fitted = attributes(predict(svmfit.flex, dat[train,], decision.values =T))$decision.values


However, these ROC curves are all on the training data. We are really more interested in the level of prediction accuracy on the test data. When we compute the ROC curves on the test data, the model with γ = 2 appears to provide the most accurate results.

> fitted = attributes(predict(svmfit.opt, dat[-train,], decision.values =T))$decision.values > rocplot(fitted, dat[-train,"y"], main="Test Data") > fitted = attributes(predict(svmfit.flex, dat[-train,], decision.values =T))$decision.values
> rocplot(fitted, dat[-train,"y"], add=T, col ="red")


### 6.4 SVM with Multiple Classes

If the response is a factor containing more than two levels, then the svm() function will perform multi-class classification using the one-versus-one approach.

> set.seed(1)
> x = rbind(x, matrix(rnorm(50*2), ncol=2))
> y = c(y, rep(0 ,50))
> x[y==0,2] = x[y==0,2]+2
> dat = data.frame(x=x, y=as.factor(y))
> par(mfrow = c(1,1))
> plot(x, col=(y+1))

> svmfit = svm(y~., data=dat, kernel="radial", cost=10, gamma=1)
> plot(svmfit, dat)


The e1071 library can also be used to perform support vector regression, if the response vector that is passed in to svm() is numerical rather than a factor.

### 6.5 Application to Gene Expression Data

We now examine the Khan data set, which consists of a number of tissue samples corresponding to four distinct types of small round blue cell tumors. For each tissue sample, gene expression measurements are available. The data set consists of training data, xtrain and ytrain, and testing data, xtest and ytest.

> library(ISLR)
> names(Khan)
 "xtrain" "xtest" "ytrain" "ytest"

In this data set, there are a very large number of features relative to the number of observations. This suggests that we should use a linear kernel.

> dat = data.frame(x=Khan$xtrain, y=as.factor(Khan$ytrain))
> out = svm(y~., data=dat, kernel="linear", cost=10)
> summary(out)
> table(out$fitted, dat$y) ## check training error

> dat.te = data.frame(x=Khan$xtest, y=as.factor(Khan$ytest))
> pred.te = predict(out, newdata=dat.te)
> table(pred.te, dat.te\$y) ## check test error