Multi-label Classification based on Association Rules with Application to Scene Classification

In this paper, a multi-label classification based on association rules is proposed. To deal with multiple class labels problem which is hard to settle by existing methods, this algorithm decomposes multi-label data to mine single-label rules, then combines labels with the same attributes to generate multi-label rules. It extracts partial dataset features to build the initial classifier through assembling, and conducts classification prediction by assembling the classifiers. Thus, the computational complexity caused by the high dimensional attributes decreases while the performance and efficiency increases. Then, the multi-label classification algorithm based on association rules which achieve good performance in an application to scene classification.