Effect of instance selection algorithms on prediction error of numerical variables
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| Autores: | , |
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| Formato: | artículo original |
| Estado: | Versión publicada |
| Data de Publicación: | 2024 |
| Descripción: | The main objective of this study is to analyze the effect of instance selection (IS) algorithms on the prediction error in regression tasks with machine learning. Six algorithms were evaluated; four from literature and two are new variants of one of them. Different percentages and magnitudes of noise were added to the output variable of 52 datasets to evaluate the algorithms. The results show that not all IS algorithms are effective. RegENN and its variants improve the prediction error (RMSE) of the regression task in most datasets for high percentages and magnitudes of noise. However, when the magnitude and percentage of noise are lower, for example, 10%-10%, 50%-10%, or 10%-30%, there is no evidence of improvement in most datasets. Other results are presented to answer four new questions about the performance of the algorithms. |
| País: | Portal de Revistas TEC |
| Institución: | Instituto Tecnológico de Costa Rica |
| Repositorio: | Portal de Revistas TEC |
| Idioma: | Español |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/6937 |
| Acceso en liña: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/6937 |
| Palabra crave: | Instance selection algorithms regression task machine learning noise Algoritmos de selección de instancias tareas de regresión aprendizaje automático ruido |