DBSMOTE: Density-based synthetic minority over-sampling technique
A dataset exhibits the class imbalance problem when a target class has a very small number of instances relative to other classes. A trivial classifier typically fails to detect a minority class due to its extremely low incidence rate. In this paper, a new over-sampling technique called DBSMOTE is p...
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th-cmuir.6653943832-386312015-06-16T07:53:41Z DBSMOTE: Density-based synthetic minority over-sampling technique Bunkhumpornpat,C. Sinapiromsaran,K. Lursinsap,C. Artificial Intelligence A dataset exhibits the class imbalance problem when a target class has a very small number of instances relative to other classes. A trivial classifier typically fails to detect a minority class due to its extremely low incidence rate. In this paper, a new over-sampling technique called DBSMOTE is proposed. Our technique relies on a density-based notion of clusters and is designed to over-sample an arbitrarily shaped cluster discovered by DB-SCAN. DBSMOTE generates synthetic instances along a shortest path from each positive instance to a pseudo-centroid of a minority-class cluster. Consequently, these synthetic instances are dense near this centroid and are sparse far from this centroid. Our experimental results show that DBSMOTE improves precision, F-value, and AUC more effectively than SMOTE, Borderline-SMOTE, and Safe-Level-SMOTE for imbalanced datasets. © 2011 Springer-Verlag. 2015-06-16T07:53:41Z 2015-06-16T07:53:41Z 2012-04-01 Article 0924669X 2-s2.0-84862140885 10.1007/s10489-011-0287-y http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84862140885&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/38631 Springer Netherlands |
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Artificial Intelligence Bunkhumpornpat,C. Sinapiromsaran,K. Lursinsap,C. DBSMOTE: Density-based synthetic minority over-sampling technique |
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A dataset exhibits the class imbalance problem when a target class has a very small number of instances relative to other classes. A trivial classifier typically fails to detect a minority class due to its extremely low incidence rate. In this paper, a new over-sampling technique called DBSMOTE is proposed. Our technique relies on a density-based notion of clusters and is designed to over-sample an arbitrarily shaped cluster discovered by DB-SCAN. DBSMOTE generates synthetic instances along a shortest path from each positive instance to a pseudo-centroid of a minority-class cluster. Consequently, these synthetic instances are dense near this centroid and are sparse far from this centroid. Our experimental results show that DBSMOTE improves precision, F-value, and AUC more effectively than SMOTE, Borderline-SMOTE, and Safe-Level-SMOTE for imbalanced datasets. © 2011 Springer-Verlag. |
format |
Article |
author |
Bunkhumpornpat,C. Sinapiromsaran,K. Lursinsap,C. |
author_facet |
Bunkhumpornpat,C. Sinapiromsaran,K. Lursinsap,C. |
author_sort |
Bunkhumpornpat,C. |
title |
DBSMOTE: Density-based synthetic minority over-sampling technique |
title_short |
DBSMOTE: Density-based synthetic minority over-sampling technique |
title_full |
DBSMOTE: Density-based synthetic minority over-sampling technique |
title_fullStr |
DBSMOTE: Density-based synthetic minority over-sampling technique |
title_full_unstemmed |
DBSMOTE: Density-based synthetic minority over-sampling technique |
title_sort |
dbsmote: density-based synthetic minority over-sampling technique |
publisher |
Springer Netherlands |
publishDate |
2015 |
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http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84862140885&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/38631 |
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