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.
tensor products label dependence multilabel kernel methods