Logistic evolutionary product-unit neural network classifier: the case of agrarian efficiency

Research areas:

• Methodology - Machine learning

Year:

2015

Type of Publication:

Article

Keywords:

Neural networks, Logistic regression, Classification, Product-unit, Evolutionary algorithms, Agrarian technical efficiency, Data envelopment analysi

Authors:

• Torres-Jiménez, Mercedes
• García-Alonso, Carlos
• Sánchez-Monedero, Javier
• Millán-Lara, Salud
• Hervás-Martínez, César

Journal:

Progress in Artificial Intelligence

Volume:

4

Number:

3--4

Pages:

59-67

ISSN:

2012- - 2

BibTex:

@article{Torres2015pai, author = "Mercedes Torres-Jim{\'e}nez and Carlos Garc{\'i}a-Alonso and Javier S{\'a}nchez-Monedero and Salud Mill{\'a}n-Lara and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Using a high-variability sample of real agrarian enterprises previously classified into two classes (efficient and inefficient), a comparative study was carried out to demonstrate the classification accuracy of logistic regression algorithms based on evolutionary productunit neural networks. Data envelopment analysis considering variable returns to scale (BBC-DEA) was chosen to classify selected farms (220 olive tree farms in dry farming) as efficient or inefficient using surveyed socio-economic variables (agrarian year 2000). Once the sample was grouped by BCC-DEA, easy-to-collect descriptive variables (concerning the farm and farmer) were then used as independent variables to find a quick and reliable alternative for classifying agrarian enterprises as efficient or inefficient according to their technical efficiency. Results showed that our proposal is very promising for the classification of complex structures (farms).", doi = "10.1007/s13748-015-0068-7", issn = "2012 - 2018", journal = "Progress in Artificial Intelligence", keywords = "Neural networks, Logistic regression, Classification, Product-unit, Evolutionary algorithms, Agrarian technical efficiency, Data envelopment analysi", number = "3--4", pages = "59--67", title = "{L}ogistic evolutionary product-unit neural network classifier: the case of agrarian efficiency", url = "http://dx.doi.org/10.1007/s13748-015-0068-7", volume = "4", year = "2015", }

Abstract:

Using a high-variability sample of real agrarian enterprises previously classified into two classes (efficient and inefficient), a comparative study was carried out to demonstrate the classification accuracy of logistic regression algorithms based on evolutionary productunit neural networks. Data envelopment analysis considering variable returns to scale (BBC-DEA) was chosen to classify selected farms (220 olive tree farms in dry farming) as efficient or inefficient using surveyed socio-economic variables (agrarian year 2000). Once the sample was grouped by BCC-DEA, easy-to-collect descriptive variables (concerning the farm and farmer) were then used as independent variables to find a quick and reliable alternative for classifying agrarian enterprises as efficient or inefficient according to their technical efficiency. Results showed that our proposal is very promising for the classification of complex structures (farms).