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...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universidad de Oviedo (UNIOVI) |
| Repositorio: | RUO. Repositorio Institucional de la Universidad de Oviedo |
| Idioma: | inglés |
| OAI Identifier: | oai:digibuo.uniovi.es:10651/35740 |
| Acceso en línea: | http://hdl.handle.net/10651/35740 https://dx.doi.org/10.1016/j.ins.2015.08.055 |
| Access Level: | acceso abierto |
| Palabra clave: | Multilabel Label dependence Tensor products Kernel methods |
| Sumario: | 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 |
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