Using tensor products to detect unconditional label dependence in multilabel classifications

Multilabel (ML) classification tasks consist of assigning a set of labels to each input. It is well known that detecting label dependencies is crucial in order to improve the performance in ML problems. In this paper, we study a new kernel approach to take into account unconditional label dependence between labels. The aim is to improve the performance measured by a micro-averaged loss function. The core idea is to transform a ML task into a binary classification problem whose inputs are drawn from a tensor space of the original input space and a representation of the labels. In this joint feature space we define a kernel to explicitly involve both labels and object descriptions. In addition to the theoretical contributions, the experimental results of this study provide an interesting conclusion: the performance in terms of Hamming Loss can be improved when unconditional label dependence is considered, as our method does. We report a thoroughly experimentation carried out with real world domains and several synthetic datasets devised to analyze the effect of exploiting label dependence in scenarios with different degrees of dependency.