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DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique

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Abstract

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 DBSCAN. 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.

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand

    Chumphol Bunkhumpornpat, Krung Sinapiromsaran & Chidchanok Lursinsap

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  1. Chumphol Bunkhumpornpat
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  2. Krung Sinapiromsaran
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  3. Chidchanok Lursinsap
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Correspondence to Chumphol Bunkhumpornpat.

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Bunkhumpornpat, C., Sinapiromsaran, K. & Lursinsap, C. DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique. Appl Intell 36, 664–684 (2012). https://doi.org/10.1007/s10489-011-0287-y

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