  Multi-label (ML) data deals with multiple classes associated with individual
samples at the same time. This leads to the co-occurrence of several classes
repeatedly, which indicates some existing correlation among them. In this
article, the correlation among classes has been explored to improve the
classification performance of existing ML classifiers. A novel approach of
frequent label-set mining has been proposed to extract these correlated classes
from the label-sets of the data. Both co-presence (CP) and co-absence (CA) of
classes have been taken into consideration. The rules mined from the ML data
has been further used to incorporate class correlation information into
existing ML classifiers. The soft scores generated by an ML classifier are
modified through a novel approach using the CP-CA rules. A concept of certain
and uncertain scores has been defined here, where the proposed method aims to
improve the uncertain scores with the help of the certain scores and their
corresponding CP-CA rules. This has been experimentally analysed on ten ML
datasets for three ML existing classifiers which shows substantial improvement
in their overall performance.
