A Study of Prototype Selection Algorithms for Nearest Neighbour in Class-Imbalanced Problems
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Título: | A Study of Prototype Selection Algorithms for Nearest Neighbour in Class-Imbalanced Problems |
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Autor/es: | Valero-Mas, Jose J. | Calvo-Zaragoza, Jorge | Rico-Juan, Juan Ramón | Iñesta, José M. |
Grupo/s de investigación o GITE: | Reconocimiento de Formas e Inteligencia Artificial |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | kNN | Imbalanced data | Prototype selection |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | 12-may-2017 |
Editor: | Springer, Cham |
Cita bibliográfica: | Valero-Mas J.J., Calvo-Zaragoza J., Rico-Juan J.R., Iñesta J.M. (2017) A Study of Prototype Selection Algorithms for Nearest Neighbour in Class-Imbalanced Problems. In: Alexandre L., Salvador Sánchez J., Rodrigues J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science, vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_37 |
Resumen: | Prototype Selection methods aim at improving the efficiency of the Nearest Neighbour classifier by selecting a set of representative examples of the training set. These techniques have been studied in situations in which the classes at issue are balanced, which is not representative of real-world data. Since class imbalance affects the classification performance, data-level balancing approaches that artificially create or remove data from the set have been proposed. In this work, we study the performance of a set of prototype selection algorithms in imbalanced and algorithmically-balanced contexts using data-driven approaches. Results show that the initial class balance remarkably influences the overall performance of prototype selection, being generally the best performances found when data is algorithmically balanced before the selection stage. |
Patrocinador/es: | Work partially supported by the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013-48152-C2-1-R supported by EU FEDER funds), the Spanish Ministerio de Educación, Cultura y Deporte through FPU program (AP2012–0939) and the Vicerrectorado de Investigación, Desarrollo e Innovación de la Universidad de Alicante through FPU program (UAFPU2014–5883). |
URI: | http://hdl.handle.net/10045/118821 |
ISBN: | 978-3-319-58837-7 | 978-3-319-58838-4 |
ISSN: | 0302-9743 |
DOI: | 10.1007/978-3-319-58838-4_37 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/conferenceObject |
Derechos: | © Springer International Publishing AG 2017 |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1007/978-3-319-58838-4_37 |
Aparece en las colecciones: | INV - GRFIA - Comunicaciones a Congresos, Conferencias, etc. |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Valero-Mas_etal_IbPRIA2017_final.pdf | Versión final (acceso restringido) | 288,24 kB | Adobe PDF | Abrir Solicitar una copia |
Valero-Mas_etal_IbPRIA2017_preprint.pdf | Preprint (acceso abierto) | 313,7 kB | Adobe PDF | Abrir Vista previa |
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