A New Under-Sampling Method to Face Class Overlap and Imbalance
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Otros documentos de la autoría: Guzmán-Ponce, Angélica; Valdovinos Rosas, Rosa María; Sánchez Garreta, Josep Salvador; Marcial-Romero, J. Raymundo
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Título
A New Under-Sampling Method to Face Class Overlap and ImbalanceAutoría
Fecha de publicación
2020-07-07Editor
MDPIISSN
2076-3417Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.mdpi.com/2076-3417/10/15/5164Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Class overlap and class imbalance are two data complexities that challenge the design of effective classifiers in Pattern Recognition and Data Mining as they may cause a significant loss in performance. Several solutions ... [+]
Class overlap and class imbalance are two data complexities that challenge the design of effective classifiers in Pattern Recognition and Data Mining as they may cause a significant loss in performance. Several solutions have been proposed to face both data difficulties, but most of these approaches tackle each problem separately. In this paper, we propose a two-stage under-sampling technique that combines the DBSCAN clustering algorithm to remove noisy samples and clean the decision boundary with a minimum spanning tree algorithm to face the class imbalance, thus handling class overlap and imbalance simultaneously with the aim of improving the performance of classifiers. An extensive experimental study shows a significantly better behavior of the new algorithm as compared to 12 state-of-the-art under-sampling methods using three standard classification models (nearest neighbor rule, J48 decision tree, and support vector machine with a linear kernel) on both real-life and synthetic databases. [-]
Publicado en
Applied Sciences, 2020, vol. 10, no 15Proyecto de investigación
Universitat Jaume I: UJI-B2018-49; Consejo Nacional de Ciencia y Tecnología: 702275Derechos de acceso
info:eu-repo/semantics/openAccess
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