Question

Here is an example of my problem

library(RWeka)
iris <- read.arff("iris.arff")

Perform nfolds to obtain the proper accuracy of the classifier.

m<-J48(class~., data=iris)
e<-evaluate_Weka_classifier(m,numFolds = 5)
summary(e)

The results provided here are obtained by building the model with a part of the dataset and testing it with another part, therefore provides accurate precision

Now I Perform AdaBoost to optimize the parameters of the classifier

m2 <- AdaBoostM1(class ~. , data = temp ,control = Weka_control(W = list(J48, M = 30)))
summary(m2)

The results provided here are obtained by using the same dataset for building the model and also the same ones used for evaluating it, therefore the accuracy is not representative of real life precision in which we use other instances to be evaluated by the model. Nevertheless this procedure is helpful for optimizing the model that is built.

The main problem is that I can not optimize the model built, and at the same time test it with data that was not used to build the model, or just use a nfold validation method to obtain the proper accuracy.

Answer 1

I guess you misinterprete the function of evaluate_Weka_classifier. In both cases, evaluate_Weka_classifier does only the cross-validation based on the training data. It doesn’t change the model itself. Compare the confusion matrices of following code:

m<-J48(Species~., data=iris)
e<-evaluate_Weka_classifier(m,numFolds = 5)
summary(m)
e


m2 <- AdaBoostM1(Species ~. , data = iris ,
       control = Weka_control(W = list(J48, M = 30)))
e2 <- evaluate_Weka_classifier(m2,numFolds = 5)
summary(m2)
e2

In both cases, the summary gives you the evaluation based on the training data, while the function evaluate_Weka_classifier() gives you the correct crossvalidation. Neither for J48 nor for AdaBoostM1 the model itself gets updated based on the crossvalidation.

Now regarding the AdaBoost algorithm itself : In fact, it does use some kind of “weighted crossvalidation” to come to the final classifier. Wrongly classified items are given more weight in the next building step, but the evaluation is done using equal weight for all observations. So using crossvalidation to optimize the result doesn’t really fit into the general idea behind the adaptive boosting algorithm.

If you want a true crossvalidation using a training set and a evaluation set, you could do the following :

id <- sample(1:length(iris$Species),length(iris$Species)*0.5)
m3 <- AdaBoostM1(Species ~. , data = iris[id,] ,
      control = Weka_control(W = list(J48, M=5)))

e3 <- evaluate_Weka_classifier(m3,numFolds = 5)
# true crossvalidation
e4 <- evaluate_Weka_classifier(m3,newdata=iris[-id,])

summary(m3)
e3
e4

If you want a model that gets updated based on a crossvalidation, you’ll have to go to a different algorithm, eg randomForest() from the randomForest package. That collects a set of optimal trees based on crossvalidation. It can be used in combination with the RWeka package as well.

edit : corrected code for a true crossvalidation. Using the subset argument has effect in the evaluate_Weka_classifier() as well.