Peralta, BillySaavedra, ArielCaro, LuisSoto, Alvaro2022-10-242022-10-242019-021099-4300https://repositorio.unab.cl/xmlui/handle/ria/24413Indexación: ScopusToday, there is growing interest in the automatic classification of a variety of tasks, such as weather forecasting, product recommendations, intrusion detection, and people recognition. "Mixture-of-experts" is a well-known classification technique; it is a probabilistic model consisting of local expert classifiers weighted by a gate network that is typically based on softmax functions, combined with learnable complex patterns in data. In this scheme, one data point is influenced by only one expert; as a result, the training process can be misguided in real datasets for which complex data need to be explained by multiple experts. In this work, we propose a variant of the regular mixture-of-experts model. In the proposed model, the cost classification is penalized by the Shannon entropy of the gating network in order to avoid a "winner-takes-all" output for the gating network. Experiments show the advantage of our approach using several real datasets, with improvements in mean accuracy of 3-6% in some datasets. In future work, we plan to embed feature selection into this model. © 2019 by the authors.enClassificationEntropyMixture-of-expertsRegularizationArtículoAtribución 4.0 Internacional (CC BY 4.0)10.3390/e21020190