Abstract
The goal of the Label Ranking (LR) Problem is to learn preference models that predict the preferred ranking of class labels for a given unlabelled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In particular, fine-tuned instance-based algorithms have exhibited a remarkable performance, specially when the model is trained with complete rankings, while model-based algorithms (e.g. decision trees) have been proved to be more robust when some data is missing, that is, the model is trained with incomplete rankings.
Probabilistic Graphical Models (PGMs, e.g. Bayesian networks) have not been considered to deal with this problem because of the difficulty to model permutations in that framework. In this paper, we propose a Hidden Naive Bayes classifier (HNB) to cope with the LR problem. By introducing the hidden variable we can design a hybrid Bayesian network in which several types of distributions can be combined, in particular, the Mallows distribution, which is a well-known distribution to deal with permutations. The experimental evaluation shows that the HNB classifier is competitive in accuracy when compared with Label Ranking (decision) Trees, being, moreover, considerably faster.
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Acknowledgements
This work has been partially funded by FEDER funds, the Spanish Government (AEI/MINECO) through the project TIN2016-77902-C3-1-P and the Regional Government (JCCM) by SBPLY/17/180501/000493.
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Alfaro, J.C., Rodrigo, E.G., Aledo, J.Á., Gámez, J.A. (2019). A Probabilistic Graphical Model-Based Approach for the Label Ranking Problem. In: Kern-Isberner, G., Ognjanović, Z. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2019. Lecture Notes in Computer Science(), vol 11726. Springer, Cham. https://doi.org/10.1007/978-3-030-29765-7_29
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