Abstract
Given the increase of data being collected, there is a need to explore the use of tools to automate the recognition and extraction of patterns within some targeted data. The present work explores the use of a neuro-fuzzy classifier for the multi-factor productivity from the manufacturing sector in the Mexican economy. The chosen data set contains the time series for the variables: Sale Value of products, Wages, Work Force, Days Worked, and Hours Worked. The data is taken from the Banco de Información Económica at the Instituto Nacional de Estadística y Geografía. The neuro-fuzzy system is implemented on top of the Neuroph library extending on the ideas behind the Neuro-Fuzzy Reasoner. A sample run tends to assign the same values given by a visual inspection.
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Becerra-Gaviño, G., Barbosa-Santillán, L.I. (2014). Neuro-Fuzzy Data Mining Mexico’s Economic Data. In: Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_79
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DOI: https://doi.org/10.1007/978-3-319-12568-8_79
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