Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem

Abstract

Abstract. The class imbalanced problem occurs in various disciplines when one of target classes has a tiny number of instances comparing to other classes. A typical classifier normally ignores or neglects to detect a minority class due to the small number of class instances. SMOTE is one of over-sampling techniques that remedies this situation. It generates minority instances within the overlapping regions. However, SMOTE randomly synthesizes the minority instances along a line joining a minority instance and its selected nearest neighbours, ignoring nearby majority instances. Our technique called SafeLevel-SMOTE carefully samples minority instances along the same line with different weight degree, called safe level. The safe level computes by using nearest neighbour minority instances. By synthesizing the minority instances more around larger safe level, we achieve a better accuracy performance than SMOTE and Borderline-SMOTE.

Improving Risk Predictions by Preprocessing Imbalanced Credit Data

“…Numerous modifications to the original SMOTE have been proposed with the aim of determining the region in which the positive examples should be generated. For example, the Safe-Level SMOTE (SL-SMOTE) algorithm [3] calculates a "safe level" coefficient (sl) for each positive example, which is defined as the number of positive cases in its k neighbors. If sl ≈ 0, such an example is considered as noise; if sl ≈ k, then the example may be located in a safe region of the minority class.…”

EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data

“…Several later proposals (e.g. [31,32,33]) are modifications of SMOTE, replacing some of its random components by more complex procedures.…”

On the suitability of resampling techniques for the class imbalance problem in credit scoring

“…Numerous modifications to the original SMOTE have been proposed in the literature, most of them pursuing to determine the region in which the positive examples should be generated. Thus, the Safe-Level SMOTE (SL-SMOTE) algorithm (Bunkhumpornpat et al 2009) calculates a "safe level" coefficient (sl) for each example from the minority class, which is defined as the number of other minority class instances among its k neighbors. If the coefficient sl is equal or close to 0, such an example is considered as noise; if sl is close to k, then this example may be located in a safe region of the minority class.…”