One-class classification with SVM in R

I think this is what you want:

library(e1071)data(iris)df <- irisdf <- subset(df ,  Species=='setosa')  #choose only one of the classesx <- subset(df, select = -Species) #make x variablesy <- 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 setpred <- 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:  27Number 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 

A little bit elaborated code with accuracy: train=78.125 test= 91.53:

library(e1071)library(caret)library(NLP)library(tm)data(iris)iris$SpeciesClass[iris$Species=="versicolor"] <- "TRUE"iris$SpeciesClass[iris$Species!="versicolor"] <- "FALSE"trainPositive<-subset(iris,SpeciesClass=="TRUE")testnegative<-subset(iris,SpeciesClass=="FALSE")inTrain<-createDataPartition(1:nrow(trainPositive),p=0.6,list=FALSE)trainpredictors<-trainPositive[inTrain,1:4]trainLabels<-trainPositive[inTrain,6]testPositive<-trainPositive[-inTrain,]testPosNeg<-rbind(testPositive,testnegative)testpredictors<-testPosNeg[,1:4]testLabels<-testPosNeg[,6]svm.model<-svm(trainpredictors,y=NULL,               type='one-classification',               nu=0.10,               scale=TRUE,               kernel="radial")svm.predtrain<-predict(svm.model,trainpredictors)svm.predtest<-predict(svm.model,testpredictors)# confusionMatrixTable<-table(Predicted=svm.pred,Reference=testLabels)# confusionMatrix(confusionMatrixTable,positive='TRUE')confTrain<-table(Predicted=svm.predtrain,Reference=trainLabels)confTest<-table(Predicted=svm.predtest,Reference=testLabels)confusionMatrix(confTest,positive='TRUE')print(confTrain)print(confTest)