Cluster-Based Minority Over-Sampling for Imbalanced Datasets

Abstract

Kamthorn PUNTUMAPON†a) , Student Member, Thanawin RAKTHAMAMON †b) , Nonmember, and Kitsana WAIYAMAI †c) , Member SUMMARY Synthetic over-sampling is a well-known method to solve class imbalance by modifying class distribution and generating synthetic samples. A large number of synthetic over-sampling techniques have been proposed; however, most of them suffer from the over-generalization problem whereby synthetic minority class samples are generated into the majority class region. Learning from an over-generalized dataset, a classifier could misclassify a majority class member as belonging to a minority class. In this paper a method called TRIM is proposed to overcome the over-generalization problem. The idea is to identify minority class regions that compromise between generalization and overfitting. TRIM identifies all the minority class regions in the form of clusters. Then, it merges a large number of small minority class clusters into more generalized clusters. To enhance the generalization ability, a cluster connection step is proposed to avoid over-generalization toward the majority class while increasing generalization of the minority class. As a result, the classifier is able to correctly classify more minority class samples while maintaining its precision. Compared with SMOTE and extended versions such as Borderline-SMOTE, experimental results show that TRIM exhibits significant performance improvement in terms of F-measure and AUC. TRIM can be used as a preprocessing step for synthetic over-sampling methods such as SMOTE and its extended versions. key words: imbalanced data, cluster-based minority over-sampling, synthetic minority over-sampling

CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification

“…Similarly, Seoane Santos et al [38] handled patients data by clustering the minority class instances and then rebalanced the data using SMOTE. Puntumapon et al [39] proposed a new method called TRIM, as a preprocessing stage before applying oversampling methods such as SMOTE or one of its extensions. Lim et al [40] implemented an evolutionary ensemble learning framework by clustering the minority class instances using mini-batch k-means and hierarchical agglomerative clustering before generating synthetic samples.…”

Sequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors

“…Such an imbalance impacted the accuracy in that the algorithm could declare the defect-free to the defective data. To overcome this problem, the data processing techniques named over-sampling and under-sampling were introduced [38]. The over-sampling method could generate the artificial defect data along the line segment between other defective data.…”

Pruning-based oversampling technique with smoothed bootstrap resampling for imbalanced clinical dataset of Covid-19

“…TRIM is a method that aims to avoid overgeneralization of data. The basic idea is to identify the collection of minority data with the best compromise between generalizability and precision between data ( Puntumapon et al, 2016 ). Equation (1) is used to measure the precision and generalization of data.…”