A cluster-based SMOTE both-sampling (CSBBoost) ensemble algorithm for classifying imbalanced data

Abstract

In this paper, a Cluster-based Synthetic minority oversampling technique (SMOTE) Both-sampling (CSBBoost) ensemble algorithm is proposed for classifying imbalanced data. In this algorithm, a combination of over-sampling, under-sampling, and different ensemble algorithms, including Extreme Gradient Boosting (XGBoost), random forest, and bagging, is employed in order to achieve a balanced dataset and address the issues including redundancy of data after over-sampling, information loss in under-sampling, and random sample selection for sampling and sample generation. The performance of the proposed algorithm is evaluated and compared to different state-of-the-art competing algorithms based on 20 benchmark imbalanced datasets in terms of the harmonic mean of precision and recall (F1) and area under the receiver operating characteristics curve (AUC) measures. Based on the results, the proposed CSBBoost algorithm performs significantly better than the competing algorithms. In addition, a real-world dataset is used to demonstrate the applicability of the proposed algorithm.

Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia

“…To address this challenge, the field of ML has developed advanced strategies [ 39 , 40 ]. Class balancing techniques, such as random sampling [ 41 ], synthetic minority oversampling technique (SMOTE) [ 42 , 43 ], and other methodologies [ 44 ], have shown promise in reducing data disparities through improved ML model training [ 45 ]. For instance, these approaches have already been used in a variety of research contexts, including medical diagnosis, gait and image analysis [ 46 , 47 , 48 , 49 , 50 ], and have shown significant improvements in disease detection and clinical outcome prediction [ 51 , 52 ].…”