R中SVM的一类分类

我想这就是你想要的:

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

输出:

-摘要:

> 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(由于视觉原因,此处仅显示了一些预测(其中Species =='setosa')):

> 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 

精确地编写了一些代码：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)