Logistic evolutionary product-unit neural networks: Innovation capacity of poor Guatemalan households |
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Authors: | Carlos R García-Alonso Jorge Guardiola César Hervás-Martínez |
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Institution: | 1. Department of Management and Quantitative Methods (ETEA) University of Cordoba, Escritor Castilla Aguayo 14004 Cordoba, Spain;2. Department of Applied Economics, University of Granada, Spain;3. Department of Computing and Numerical Analysis of the University of Córdoba, Spain |
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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. |
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Keywords: | Neural networks Logistic regression Product-unit Evolutionary algorithms Sustainability Poor households |
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