A proposal for mixture of experts with entropic regularization

Billy Peralta, Ariel Saavedra, Luis Caro

Resultado de la investigación: Conference contribution

Resumen

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.

Idioma originalEnglish
Título de la publicación alojada2017 43rd Latin American Computer Conference, CLEI 2017
EditoresRodrigo Santos, Hector Monteverde
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas1-9
Número de páginas9
Volumen2017-January
ISBN (versión digital)9781538630570
DOI
EstadoPublished - 18 dic 2017
Evento43rd Latin American Computer Conference, CLEI 2017 - Cordoba, Argentina
Duración: 4 sep 20178 sep 2017

Conference

Conference43rd Latin American Computer Conference, CLEI 2017
PaísArgentina
CiudadCordoba
Período4/09/178/09/17

Huella dactilar

expert
Intrusion detection
Face recognition
Pattern recognition
Feature extraction
Classifiers
Entropy
Neural networks
pattern recognition
entropy
neural network
Costs
event
costs

ASJC Scopus subject areas

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

Citar esto

Peralta, B., Saavedra, A., & Caro, L. (2017). A proposal for mixture of experts with entropic regularization. En R. Santos, & H. Monteverde (Eds.), 2017 43rd Latin American Computer Conference, CLEI 2017 (Vol. 2017-January, pp. 1-9). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CLEI.2017.8226425
Peralta, Billy ; Saavedra, Ariel ; Caro, Luis. / A proposal for mixture of experts with entropic regularization. 2017 43rd Latin American Computer Conference, CLEI 2017. editor / Rodrigo Santos ; Hector Monteverde. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-9
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Peralta, B, Saavedra, A & Caro, L 2017, A proposal for mixture of experts with entropic regularization. En R Santos & H Monteverde (eds.), 2017 43rd Latin American Computer Conference, CLEI 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-9, 43rd Latin American Computer Conference, CLEI 2017, Cordoba, Argentina, 4/09/17. https://doi.org/10.1109/CLEI.2017.8226425

A proposal for mixture of experts with entropic regularization. / Peralta, Billy; Saavedra, Ariel; Caro, Luis.

2017 43rd Latin American Computer Conference, CLEI 2017. ed. / Rodrigo Santos; Hector Monteverde. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-9.

Resultado de la investigación: Conference contribution

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Peralta B, Saavedra A, Caro L. A proposal for mixture of experts with entropic regularization. En Santos R, Monteverde H, editores, 2017 43rd Latin American Computer Conference, CLEI 2017. Vol. 2017-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-9 https://doi.org/10.1109/CLEI.2017.8226425