Logistic evolutionary product-unit neural networks: Innovation capacity of poor Guatemalan households

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 hi...

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Bibliographic Details
Published in:European journal of operational research 2009-06, Vol.195 (2), p.543-551
Main Authors: García-Alonso, Carlos R., Guardiola, Jorge, Hervás-Martínez, César
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Comparative analysis
Decision making
Evolutionary algorithms
Exact sciences and technology
Logistic regression
Logistics
Low income groups
Neural networks
Neural networks Logistic regression Product-unit Evolutionary algorithms Sustainability Poor households
Operational research and scientific management
Operational research. Management science
Poor households
Product-unit
Regression analysis
Studies
Sustainability
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Summary: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.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2008.02.013