A Robust Enhanced Ensemble Learning Method for Breast Cancer Data Diagnosis on Imbalanced Data

A Robust Enhanced Ensemble Learning Method for Breast Cancer Data Diagnosis on Imbalanced Data

Blog Article

Early breast cancer diagnosis is crucial for improving treatment outcomes for women.Addressing class imbalance in breast cancer data is essential for enhancing detection accuracy, yet traditional machine learning methods often overlook this imbalance, limiting their classification performance.To tackle this issue, we propose a robust enhanced ensemble learning method Desktop Vaporizers (REEL).

Specifically, a double-level over-sampling technology is developed to increase the diversity of synthesized minority breast cancer samples before model training, and an improved Random Forest is proposed to reconcile the bias and variance.In addition, a data-driven Mug based particle swarm optimization algorithm automatically is used to select the value of parameters for base classifiers.Experimental results on breast cancer datasets and 19 other imbalanced datasets validate that our method outperforms other algorithms in terms of accuracy, F1 score, and AUC.

These findings confirm that our method can further improve classification accuracy and has significant application value in the diagnosis of breast cancer.