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Multi-label (ML) data deals with multiple classes associated with individualsamples at the same time. This leads to the co-occurrence of several classesrepeatedly, which indicates some existing correlation among them. In thisarticle, the correlation among classes has been explored to improve theclassification performance of existing ML classifiers. A novel approach offrequent label-set mining has been proposed to extract these correlated classesfrom the label-sets of the data. Both co-presence (CP) and co-absence (CA) ofclasses have been taken into consideration. The rules mined from the ML datahas been further used to incorporate class correlation information intoexisting ML classifiers. The soft scores generated by an ML classifier aremodified through a novel approach using the CP-CA rules. A concept of certainand uncertain scores has been defined here, where the proposed method aims toimprove the uncertain scores with the help of the certain scores and theircorresponding CP-CA rules. This has been experimentally analysed on ten MLdatasets for three ML existing classifiers which shows substantial improvementin their overall performance.

Improved Multi-label Classification with Frequent Label-set Mining and Association