Classification of one class with SVM in R

Question

I am using the e1071 package in R to build a single-class SVM model. I do not know how to do this, and I have not found a single example on the Internet.

Can someone give an example code for a characteristic, for example, the setosa class in an iris dataset with a single-class classification model, and then check all the examples in one data set (to check which examples belong to the setosa class characteristic , and which are not)?

Answer 1

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 

- Confessions (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 

Answer 2

The code with accuracy is slightly developed: 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)