K-Nearest neighbor in a classification and prediction application in the Judicial Power of Peru
Abstract: This article summarizes the main contributions of the thesis with the title “K-Nearest neighbor in a classification and prediction application in the Judicial Power of Peru". In this thesis a model is constructed using the method of the nearest k-neighbors that allows classifying and...
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Perú |
| Institución: | Universidad Nacional Mayor de San Marcos |
| Repositorio: | Revistas - Universidad Nacional Mayor de San Marcos |
| Idioma: | español |
| OAI Identifier: | oai:revistasinvestigacion.unmsm.edu.pe:article/15077 |
| Acceso en línea: | https://revistasinvestigacion.unmsm.edu.pe/index.php/matema/article/view/15077 |
| Access Level: | acceso abierto |
| Palabra clave: | clasificación supervisada y no supervisada k-vecinos más próximos clasificación no paramétrica partición validación cruzada aleatoria. supervised and unsupervised classification nearest k-neighbors nonparametric classification partition random cross-validation |
| Sumario: | Abstract: This article summarizes the main contributions of the thesis with the title “K-Nearest neighbor in a classification and prediction application in the Judicial Power of Peru". In this thesis a model is constructed using the method of the nearest k-neighbors that allows classifying and predicting the Superior Courts of Justice of Peru. Through a descriptive data analysis, the Lima Court is excluded from the study. With the remaining 30 Superior Courts, a three-group model based on unsupervised classification is generated, for which the Euclidean distance matrix that originates the classification tree is deduced. The classification model of three nearest neighbors is constructed, with partition and random cross-validation folds, which indicates; the predictor space model, the quadratic error or error index that validates the op-timal value of k = 3 neighbors, the model error and the global forecast index that measure the accuracy or accuracy of the model found, importance of the predictor, maps of quadrants and table of neighbors. |
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