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A New Under-Sampling Method to Face Class Overlap and Imbalance
dc.contributor.author | Guzmán-Ponce, Angélica | |
dc.contributor.author | Valdovinos Rosas, Rosa María | |
dc.contributor.author | Sánchez Garreta, Josep Salvador | |
dc.contributor.author | Marcial-Romero, J. Raymundo | |
dc.date.accessioned | 2020-09-04T06:32:35Z | |
dc.date.available | 2020-09-04T06:32:35Z | |
dc.date.issued | 2020-07-07 | |
dc.identifier.issn | 2076-3417 | |
dc.identifier.uri | http://hdl.handle.net/10234/189506 | |
dc.description.abstract | 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. | ca_CA |
dc.format.extent | 22 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | MDPI | ca_CA |
dc.relation.isPartOf | Applied Sciences, 2020, vol. 10, no 15 | ca_CA |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-sa/4.0/ | * |
dc.subject | class imbalance | ca_CA |
dc.subject | class overlap | ca_CA |
dc.subject | under-sampling | ca_CA |
dc.subject | clustering | ca_CA |
dc.subject | DBSCAN | ca_CA |
dc.subject | minimum spanning tree | ca_CA |
dc.title | A New Under-Sampling Method to Face Class Overlap and Imbalance | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.3390/app10155164 | |
dc.relation.projectID | Universitat Jaume I: UJI-B2018-49; Consejo Nacional de Ciencia y Tecnología: 702275 | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://www.mdpi.com/2076-3417/10/15/5164 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
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