High-Level Classification for Multi-Label Learning

Multi-label learning (MLL) addresses the problem of learning from data items which can be associated with multiple labels simultaneously. As MLL techniques are usually derived of single-label ones, they also share common drawbacks. For example, most MLL techniques perform a low-level classification, i.e., they consider only the physical features of the input data (e.g., distance, distribution, etc) in the classification process, having troubles to detect semantic relationships among the data items, like the formation pattern for example. Recent studies have shown that learning systems based on complex networks have the ability to consider not only the physical features of the data, but also structural and topological features extracted from the network connection patterns, which is known as high-level classification. In this paper, we investigate a MLL framework which combines both low-level and high-level techniques in order to improve the predictive performance of existing MLL techniques. Experiments conducted on artificial and real-world data sets highlighted the salient features of the MLL framework and also attested its good predictive performance in comparison with widely used MLL techniques, indicating that our framework may considerable improve their predictive performance.