Hello everyone,
I have a dataset where each sample needs to be classified in 5 classes and each class can take values between 0 and 4, only one of them. for example a possible target can ben [0,1,1,3,2]
I have already a working model using regression from the original continuous labels for the 5 classes (each class in [0,1] range) which in this case I binned to turn the problem in a classification problem to see if I can reach better performances, handling the new imbalanced version of the dataset due to the binning. Basically I want to see if simplifying the problem to less discrete classes can improve the performance.
do you think I should treat this problem as 5 different multiclass classifiers and in this case is there a way to write the network so I don’t have to run the same network 5 times
or more shall I encode each of the 5 classes as one hot and go for the standard classification postprocessing the output of the network splitting it accordingly to the 5 classes and then do argmax, or even using the binary_crossentropy loss? or thinking of the problem as multi label classification?
these are many ideas, what would work better in your experience?