I would like to create an ensemble classifier for a dataset and use different classification models for different subsets of features (these feature subsets are predefined as the data set I am working on has different modalities that require different algorithms/learners).

In mlr (R) I am aware of bagging (but this does not allow me to specify the feature subsets) and stacking (but this is not allowing me to specify feature subsets). Is there a way to generate such an ensemble in mlr (R)?

df <- iris %>% mutate(Species=as.character(Species)) %>% filter(Species %in% c('setosa', 'versicolor'))
tsk1 <- makeClassifTask(data = df[,c('Sepal.Length', 'Sepal.Width', 'Species')], target = "Species")
tsk2 <- makeClassifTask(data = df[,c('Petal.Length', 'Petal.Width', 'Species')], target = "Species")
lrn1 <- makeLearner('classif.rpart',predict.type = "prob")
lrn2 <- makeLearner('classif.svm',predict.type = "prob")
m1 <- train(lrn1, tsk1)
m2 <- train(lrn2, tsk2)
res1 = predict(m1, tsk1)
res2 = predict(m2, tsk2)
rowMeans(cbind(res1$data$prob.setosa, res2$data$prob.setosa))

1 Answer 1


I had the same problem. A workaround is to wrap each learner with a filter wrapper that filters a fixed set of predictors using the argument fw.mandatory.feat. See example below using the pid.task that is already integrated in mlr.


#Make a base learner
lrn <- makeLearner('classif.logreg', predict.type = 'prob')

#Make feature sets
blood <- c('glucose', 'insulin')
other <- setdiff(getTaskFeatureNames(pid.task), blood)

#make a learners that only predicts from blood parameters.
#The fw.method does not matter, but I use "variance" here because it is the fastest:
lrn.blood <- makeFilterWrapper(lrn, fw.method = 'variance', fw.abs = length(blood),
                               fw.mandatory.feat = blood) %>%

lrn.other <- makeFilterWrapper(lrn, fw.method = 'variance', fw.abs = length(other),
                               fw.mandatory.feat = other) %>%

lrns <- list(lrn.blood, lrn.other)

#Create a super learner
sl <- makeLearner('classif.plsdaCaret', ncomp = 1)

#make stacked learner
stacked <- makeStackedLearner(lrns, super.learner = sl,
                              method = 'stack.cv', predict.type = 'prob') 

#Perform cross validation
benchmark(c(lrns, list(stacked)), pid.task, cv10, auc, models = F)

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