iBRF: Improved Balanced Random Forest Classifier

arXiv:2403.09867v1 Announce Type: new
Abstract: Class imbalance poses a major challenge in different classification tasks, which is a frequently occurring scenario in many real-world applications. Data resampling is considered to be the standard approach to address this issue. The goal of the technique is to balance the class distribution by generating new samples or eliminating samples from the data. A wide variety of sampling techniques have been proposed over the years to tackle this challenging problem. Sampling techniques can also …

abstract applications arxiv balance challenge class classification classifier cs.lg data distribution issue major random resampling samples standard tasks type world