  In multi-label classification tasks, each problem instance is associated with
multiple classes simultaneously. In such settings, the correlation between
labels contains valuable information that can be used to obtain more accurate
classification models. The correlation between labels can be exploited at
different levels such as capturing the pair-wise correlation or exploiting the
higher-order correlations. Even though the high-order approach is more capable
of modeling the correlation, it is computationally more demanding and has
scalability issues. This paper aims at exploiting the high-order label
correlation within subsets of labels using a supervised learning classifier
system (UCS). For this purpose, the label powerset (LP) strategy is employed
and a prediction aggregation within the set of the relevant labels to an unseen
instance is utilized to increase the prediction capability of the LP method in
the presence of unseen labelsets. Exact match ratio and Hamming loss measures
are considered to evaluate the rule performance and the expected fitness value
of a classifier is investigated for both metrics. Also, a computational
complexity analysis is provided for the proposed algorithm. The experimental
results of the proposed method are compared with other well-known LP-based
methods on multiple benchmark datasets and confirm the competitive performance
of this method.
