there are several soft classifiers in r, such as linear discriminant analysis. Functions such as lda {MASS} show the likelihood of each case being classified to belong to each of the classes defined in training. That's prefect.
However, as far as I could see, lda {MASS} and others, such as randomForest {randomForest} only work with 'hard' training. That is, each case of the training data has to belong to a single class. For example, the famous Fisher's iris data, which can be seen just by entering "iris" (without quotes) in the R console, shows a dataframe with 150 cases (rows) and 5 variables (columns) named Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species. Each of the 150 cases is labelled with only one class in the columns Species: setosa, versicolor and virginica.
Now say that one needs to train a classifier with 'soft' labelling, and that instead of labelling each case as one of the three classes only, each case is assigned a level of membership to the classes. For example, the Fisher's iris data could show the columns Species1, Species2 and Species3. Then, a given case could show the membership 0.4, 0.6 and 0 to Species1, Species2 and Species3 respectively.
I could find something similar in Classifier for uncertain class labels. However, the answer of cbeleites is quite unclear although helpful. As far as I understood, one can weight the cases used for training but the cases that belong to more than one class have to be repeated with different weights. I was looking for something more straightforward.
There is any classifier implemented in R that accepts partial membership of cases to classes without the need of supplying two or more instances of the same case? Thank you very much!
