Temporal data classification by imprecise dynamical models
Authors: Alessandro Antonucci, Rosa, R., Alessandro Giusti and Fabio Cuzzolin
Abstract: We propose a new methodology to classify temporal data with imprecise hidden Markov models. For each sequence we learn a different model by coupling the EM algorithm with the imprecise Dirichlet model. As a model descriptor, we consider the expected value of the observable variable in the limit of stationar- ity of the Markov chain. In the imprecise case, only the bounds of this descriptor can be evaluated. In practice the sequence, which can be regarded as a trajectory in the feature space, is summarized by a hyperbox in the same space. We classify these static but interval-valued data by a credal generalization of the k-nearest neighbors algorithm. Experiments on benchmark datasets for computer vision show that the method achieves the required robustness whilst outperforming other precise and imprecise methods.
Details: In Cozman, F. and Denoeux, T. and Destercke, S. and Seidenfeld, T. (Eds.), ISIPTA '13: Proceedings of the Eighth International Symposium on Imprecise Probability: Theories and Applications. SIPTA.
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