International Research Journal of Multidisciplinary Technovation, Journal Year: 2024, Volume and Issue: unknown, P. 58 - 76

Published: April 22, 2024

Addressing the class imbalance in classification problems is particularly challenging, especially context of medical datasets where misclassifying minority samples can have significant repercussions. This study dedicated to mitigating by employing a hybrid approach that combines data-level, cost-sensitive, and ensemble methods. Through an assessment performance, measured AUC-ROC values, Sensitivity, F1-Score, G-Mean 20 data-level four cost-sensitive models on seventeen - 12 small five large, hybridized model, SMOTE-RF-CS-LR has been devised. model integrates Synthetic Minority Oversampling Technique (SMOTE), classifier Random Forest (RF), Cost-Sensitive Logistic Regression (CS-LR). Upon testing diverse imbalanced ratios, it demonstrated remarkable achieving outstanding performance values majority datasets. Further examination model's training duration time complexity revealed its efficiency, taking less than second train each dataset. Consequently, proposed not only proves be time-efficient but also exhibits robust capabilities handling imbalance, yielding results

Language: Английский