An Elastic Self-Adjusting Technique for Rare-Class Synthetic Oversampling Based on Cluster Distortion Minimization in Data Stream.

ABSTRACT: Adaptive machine learning has increasing importance due to its ability to classify a data stream and handle the changes in the data distribution. Various resources, such as wearable sensors and medical devices, can generate a data stream with an imbalanced distribution of classes. Many popular oversampling techniques have been designed for imbalanced batch data rather than a continuous stream. This work proposes a self-adjusting window to improve the adaptive classification of an imbalanced data stream based on minimizing cluster distortion. It includes two models; the first chooses only the previous data instances that preserve the coherence of the current chunk's samples. The second model relaxes the strict filter by excluding the examples of the last chunk. Both models include generatin

SUBMITTER: Fatlawi HK 

PROVIDER: S-EPMC9963940 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature