Multi-label classification using MLkNN via Meka

[Adnan Shah]

Hi,
I am new to data mining and have doubts on understanding what this excerpt from the book Multi-Label Classification Problem Analysis and Metrics actually means.

The MLDs were partitioned following a 2×5 strategy. This means that there are two repetitions with 5 folds, and that for each run 80% (4/5) of instances are >used for training and 20%(1/5) for testing. Therefore, a total of 10 runs are made for each MLD. Random sampling was used to select the instances in each fold. The full set of folds for the aforementioned five MLDs is available in the book repository

So what I get is a 10 train/test datasets in separate files, so....
Q. Has the author already manually partitioning the single dataset into 5 folds and now the partitioned datasets (train/test) need to be directly classified one-by-one?

OR

Is he referring to some automated partitioning and then batch classification via MEKA, but as far as I have searched in MEKA I couldn't find a functionality to partition a dataset in folds plus train/test splits in one single operation.

[chanda]

MEKA is built on WEKA, WEKA takes a -x parameter to specify the number of folds. Could that be what you are looking for?