A Method Non-Deterministic and Computationally Viable for Detecting Outliers in Large Datasets
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http://hdl.handle.net/10045/106950
Título: | A Method Non-Deterministic and Computationally Viable for Detecting Outliers in Large Datasets |
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Autor/es: | Fernández Oliva, Alberto | Maciá Pérez, Francisco | Berna-Martinez, Jose Vicente | Abreu Ortega, Miguel |
Grupo/s de investigación o GITE: | GrupoM. Redes y Middleware |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | Outliers | Rough sets (RS) | RS basic model (RSBM) | Variable precision rough set model (VPRSM) | Data set | Data mining |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | may-2020 |
Editor: | Institute of Information Science, Academia Sinica |
Cita bibliográfica: | Journal of Information Science and Engineering. 2020, 36(3): 671-685. doi:10.6688/JISE.202005_36(3).0012 |
Resumen: | This paper presents an outlier detection method that is based on a Variable Precision Rough Set Model (VPRSM). This method generalizes the standard set inclusion relation, which is the foundation of the Rough Sets Basic Model (RSBM). The main contribution of this research is an improvement in the quality of detection because this generalization allows us to classify when there is some degree of uncertainty. From the proposed method, a computationally viable algorithm for large volumes of data is also introduced. The experiments performed in a real scenario and a comparison of the results with the RSBM-based method demonstrate the efficiency of both the method and the algorithm in diverse contexts that involve large volumes of data. |
Patrocinador/es: | This work has been supported by grant TIN2016-78103-C2-2-R, and University of Alicante projects GRE14-02 and Smart University. |
URI: | http://hdl.handle.net/10045/106950 |
ISSN: | 1016-2364 |
DOI: | 10.6688/JISE.202005_36(3).0012 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © Institute of Information Science, Academia Sinica |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.6688/JISE.202005_36(3).0012 |
Aparece en las colecciones: | INV - GrupoM - Artículos de Revistas |
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Archivo | Descripción | Tamaño | Formato | |
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Fernandez-Oliva_etal_2020_JISE.pdf | 411,88 kB | Adobe PDF | Abrir Vista previa | |
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