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Wine quality classification using physicochemical properties along with extreme learning machines
| dc.contributor.author | Torres González, Ítalo | |
| dc.contributor.author | Zabala-Blanco, David | |
| dc.contributor.author | Ahumada-García, Roberto | |
| dc.contributor.author | Rivelli Malcó, Juan Pablo | |
| dc.contributor.author | Azurdia-Meza, Cesar A. | |
| dc.contributor.author | Palacios Játiva, Pablo | |
| dc.date.accessioned | 2024-01-11T15:07:35Z | |
| dc.date.available | 2024-01-11T15:07:35Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://repositorio.ucm.cl/handle/ucm/5174 | |
| dc.description.abstract | Chilean wine is one of the most consumed in the global market due to its excellent quality and wide variety of grape crops throughout the country, with Chile being the largest exporter of wine in the southern hemisphere. The characterization of wine quality is of vital importance for this industry, as the valuation of this product in the market depends on it. This study proposes the binary classification of wine quality (high and low quality) for both white and red varieties. To do this, we used Extreme Learning Machine (ELM) due to its training speed and acceptable performance. The evaluated ELMs are Basic ELM, Regularized ELM, and Unbalanced ELM. The results of these algorithms were measured in terms of Accuracy, Geometric Mean, and complexity expressed in training time. When comparing the performance results of these algorithms, it can be stated that the different types of ELMs have similar performance, with the Unbalanced ELM having the best performance, with almost 80 % accuracy for white varieties and 87 % accuracy for red varieties along with learning speed in the order of seconds. This research demonstrates the viability and potentiality of using ELMs for the classification of wine quality for both white and red varieties. | es_CL |
| dc.language.iso | en | es_CL |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | * |
| dc.source | Proceedings - International Conference of the Chilean Computer Science Society, SCCC,2023, 1-6 | es_CL |
| dc.subject | Training | es_CL |
| dc.subject | Industries | es_CL |
| dc.subject | Computer science | es_CL |
| dc.subject | Extreme learning machines | es_CL |
| dc.subject | Pipelines | es_CL |
| dc.subject | Globalization | es_CL |
| dc.subject | Crops | es_CL |
| dc.title | Wine quality classification using physicochemical properties along with extreme learning machines | es_CL |
| dc.type | Article | es_CL |
| dc.ucm.facultad | Facultad de Ciencias de la Ingeniería | es_CL |
| dc.ucm.indexacion | Scopus | es_CL |
| dc.ucm.uri | ieeexplore.ieee.org/document/10315740/keywords#keywords | es_CL |
| dc.ucm.doi | doi.org/10.1109/SCCC59417.2023.10315749 | es_CL |
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