A proposal for mixture of experts with entropic regularization

Billy Peralta, Ariel Saavedra, Luis Caro

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


In these days, there are a growing interest in pattern recognition for tasks as prediction of weather events, recommendation of the best route, intrusion detection or face detection. Each of these tasks can be modelled as classification problem, where a common alternative is to use an ensemble model of classification. A well-known example is given by Mixture-of-Experts model, which represents a probabilistic artificial neural network consisting of local experts classifiers weighted by a gate network, and whose combination creates an environment of competition among experts seeking to obtain patterns of the data source. We observe that this architecture assume that one gate influence only one data point, consequently the training can be misguided in real datasets where the data is better explained by multiple experts. In this work, we present a variant of regular Mixture-of-Experts model, which consists of maximizing of the entropy of gate network in addition to classification cost minimization. The results show the advantage of our approach in multiple datasets in terms of accuracy metric. As a future work, we plan to apply this idea to the Mixture-of-Experts with embedded feature selection.

Original languageEnglish
Title of host publication2017 43rd Latin American Computer Conference, CLEI 2017
EditorsRodrigo Santos, Hector Monteverde
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages9
ISBN (Electronic)9781538630570
Publication statusPublished - 18 Dec 2017
Event43rd Latin American Computer Conference, CLEI 2017 - Cordoba, Argentina
Duration: 4 Sept 20178 Sept 2017


Conference43rd Latin American Computer Conference, CLEI 2017


  • classification
  • mixture-of-experts
  • regularization

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Software
  • Education


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