A Parameter-Free Cleaning Method for SMOTE in Imbalanced Classification

Abstract

Oversampling is an efficient technique in dealing with class-imbalance problem. It addresses the problem by reduplicating or generating the minority class samples to balance the distribution between the samples of the majority and the minority class. Synthetic minority oversampling technique (SMOTE) is one of the typical representatives. During the past decade, researchers have proposed many variants of SMOTE. However, the existing oversampling methods may generate wrong minority class samples in some scenarios. Furthermore, how to effectively mine the inherent complex characteristics of imbalanced data remains a challenge. To this end, this paper proposes a parameter-free data cleaning method to improve SMOTE based on constructive covering algorithm. The dataset generated by SMOTE is first partitioned into a group of covers, then the hard-to-learn samples can be detected based on the characteristics of sample space distribution. Finally, a pair-wise deletion strategy is proposed to remove the hard-to-learn samples. The experimental results on 25 imbalanced datasets show that our proposed method is superior to the comparison methods in terms of various metrics, such as F-measure, G-mean, and Recall. Our method not only can reduce the complexity of the dataset but also can improve the performance of the classification model. INDEX TERMSImbalanced data, SMOTE, oversampling, constructive covering algorithm, data cleaning.

Addressing the Overlapping Data Problem in Classification Using the One-vs-One Decomposition Strategy

“…For example, Prati et al [25] developed a study using a set of artificial datasets showing that the degree of class overlapping has a strong correlation with class imbalance. In this scenario, the use of over-sampling methods based on SMOTE [10], [41] has shown to be very effective [4]. The large influence of overlapping on classification performance with respect to the imbalance ratio was also corroborated in the particular case in which the minority class is more represented in the overlapping region than the majority class [14], [15].…”

Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods

“…Tomek Links can be described as a method for undersampling or as a technique for cleaning up data. They can be identified as a pair of the nearest neighbors of opposite classes, which are minimally distant [64]. They are used to remove the overlapping samples that SMOTE adds.…”

An “Outside the Box” Solution for Imbalanced Data Classification

“…It combines benefits (and some limitations) of both approaches mentioned above. Some methods applying the mixed sampling technique are Selective Preprocessing of Imbalanced Data (SPIDER) [33], Optimal Genetic Algorithms based Resampling [34] (OGAR), SMOTE-ENN [35] combining SMOTE and Edited Nearest Neighbors [36], SMOTE-TL [37] combining SMOTE and Tomek Links [38], and parameter-free data cleaning method to SMOTE [39].…”