Information Fusion And Machine Learning In Spatial Prediction For Local Agricultural Markets

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations

Abstract

This research explores information fusion and data mining techniques and proposes a methodology to improve predictions based on strong associations among agricultural products, which allows prediction for future consumption in local markets in the Andean region of Ecuador using spatial prediction techniques. This commercial activity is performed using Alternative Marketing Circuits (CIALCO), seeking to establish a direct relationship between producer and consumer prices, and promote buying and selling among family groups.

Translated title of the contributionFusión de Información y Aprendizaje de Máquinas en Predicción Espacial para Mercados Agrícolas Locales
Original languageEnglish
Title of host publicationInformation Fusion And Machine Learning In Spatial Prediction For Local Agricultural Markets
Subtitle of host publicationThe PAAMS Collection - International Workshops of PAAMS 2018, Proceedings
EditorsJuan M. Corchado, Vicente Julian, Eneko Osaba Icedo, Javier Bajo, Patrycja Hoffa-Dabrowska, Ricardo Azambuja Silveira, Alberto Fernandez, Sylvain Giroux, Elena María Navarro Martínez, Philippe Mathieu, Antonio J. Castro, Nayat Sanchez-Pi, Elena del Val, Rainer Unland, Ruben Fuentes-Fernandez
PublisherSpringer Verlag
Pages235-246
Number of pages12
ISBN (Print)978-3-319-94778-5
DOIs
StatePublished - 20 Jun 2018

Publication series

NameCommunications in Computer and Information Science
Volume887
ISSN (Print)1865-0929

Keywords

  • Alternative circuits of commercialization
  • Associations mining
  • Data fusion
  • Predictive analysis

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