Mixture of experts with entropic regularization for data classification

Today, 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.

Notas

Indexación: Scopus

Palabras clave

Classification, Entropy, Mixture-of-experts, Regularization

DOI

10.3390/e21020190

URI

https://repositorio.unab.cl/xmlui/handle/ria/24413

Colecciones

FIng - Artículos de Revista

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