Title Logistic evolutionary product-unit neural networks: Innovation capacity of poor Guatemalan households
Authors GARCÍA ALONSO, CARLOS, Guardiola J., Hervás-Martínez C., GARCÍA ALONSO, CARLOS
External publication No
Means Eur. J. Oper. Res.
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
SJR Impact 2.23600
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-56249099731&doi=10.1016%2fj.ejor.2008.02.013&partnerID=40&md5=4721752f145ed9f66893583c95a03347
Publication date 01/01/2009
ISI 000261872600018
Scopus Id 2-s2.0-56249099731
DOI 10.1016/j.ejor.2008.02.013
Abstract A new logistic regression algorithm based on evolutionary product-unit (PU) neural networks is used in this paper to determine the assets that influence the decision of poor households with respect to the cultivation of non-traditional crops (NTC) in the Guatemalan Highlands. In order to evaluate high-order covariate interactions, PUs were considered to be independent variables in product-unit neural networks (PUNN) analysing two different models either including the initial covariates (logistic regression by the product-unit and initial covariate model) or not (logistic regression by the product-unit model). Our results were compared with those obtained using a standard logistic regression model and allow us to interpret the most relevant household assets and their complex interactions when adopting NTC, in order to aid in the design of rural policies. © 2008 Elsevier B.V. All rights reserved.
Keywords Evolutionary algorithms; Flow interactions; Logistics; Regression analysis; Complex interactions; Covariate; Covariates; Independent variables; Innovation capacities; Logistic regression; Logistic reg
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