How to Build Multiclass Svm in R

How to build multiclass SVM in R?

There is no direct equivalent of Multiclass SVM in e1071. Besides, all approaches to use SVM for multiclass classification use techniques like 'one vs rest' or encoding, amongst others. Here is a reference detailing most common approaches...
http://arxiv.org/ftp/arxiv/papers/0802/0802.2411.pdf

If you want to use e1071 for multiclass SVM, you best can create 26 svm models, one for each class, and use the probability score to predict. This approach should be good enough for handwritten pattern recognition.

How to perform multi-class classification using 'svm' of e1071 package in R

The iris dataset contains three class labels: "Iris setosa", "Iris virginica" and "Iris versicolor". To employ a balanced one-against-one classification strategy with svm, you could train three binary classifiers:

The first classifier's training set only contains the "Iris setosa" and "Iris virginica" instances. The second classifier's training set only contains the "Iris setosa" and the "Iris versicolor" instances. The third classifier's training set--I guess by now you'll know already--contains only the "Iris virginica" and the "Iris versicolor" instances.

To classify an unknown instance, you apply all three classifiers. A simple voting strategy could then select the most frequently assigned class label, a more sophisticated may also consider the svm confidence scores for each assigned class label.

Edit (This principle works out of the box with svm):

# install.packages( 'e1071' )
library( 'e1071' )
data( iris )
model <- svm( iris$Species~., iris )
res <- predict( model, newdata=iris )

How to test data with R e1071 SVM multiclass

Factors in train and test dataset are different here so you would need to fix it first.

library(e1071)
#sample data
train_data <- data.frame(V1 = c("sunny","sunny sunny","rainy","rainy rainy","cloud","cloud cloud"),
                         V2= c("yes","yes","no","no","maybe","maybe"))
test_data <- data.frame(V1 = c("sunny","rainy","hello","cloud"))

#fix levels in train_data & test_data dataset before running model
train_data$ind <- "train"
test_data$ind <- "test"
merged_data <- rbind(train_data[,-grep("V2", colnames(train_data))],test_data)
#train data
train <- merged_data[merged_data$ind=="train",]
train$V2 <- train_data$V2
train <- train[,-grep("ind", colnames(train))]
#test data
test <- merged_data[merged_data$ind=="test",]
test <- data.frame(V1 = test[,-grep("ind", colnames(test))])

#svm model
svm_model <- svm(V2 ~ ., data = train, probability=TRUE)
summary(svm_model)
train_pred <- predict(svm_model,train["V1"])
table(train_pred,train$V2)

#prediction on test data
test$test_pred <- predict(svm_model,test)
test

Hope this helps!

One-class classification with SVM in R

I think this is what you want:

library(e1071)
data(iris)
df <- iris

df <- subset(df ,  Species=='setosa')  #choose only one of the classes

x <- subset(df, select = -Species) #make x variables
y <- df$Species #make y variable(dependent)
model <- svm(x, y,type='one-classification') #train an one-classification model 

print(model)
summary(model) #print summary

# test on the whole set
pred <- predict(model, subset(iris, select=-Species)) #create predictions

Output:

-Summary:

> summary(model)

Call:
svm.default(x = x, y = y, type = "one-classification")

Parameters:
   SVM-Type:  one-classification 
 SVM-Kernel:  radial 
      gamma:  0.25 
         nu:  0.5 

Number of Support Vectors:  27

Number of Classes: 1

-Predictions (only some of the predictions are shown here (where Species=='setosa') for visual reason):

> pred
    1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18    19    20    21    22 
 TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE 
   23    24    25    26    27    28    29    30    31    32    33    34    35    36    37    38    39    40    41    42    43    44 
FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE 
   45    46    47    48    49    50 
FALSE  TRUE  TRUE  TRUE  TRUE  TRUE 

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