Mixture of Experts with Entropic Regularization for Data Classification

• Main Authors: Billy Peralta, Ariel Saavedra, Luis Caro, Alvaro Soto
• Format: Article
• Language: English
• Published: MDPI AG 2019-02-01
• Series: Entropy
• Subjects: mixture-of-experts; regularization; entropy; classification
• Online Access: https://www.mdpi.com/1099-4300/21/2/190

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.