A hybrid classifier combining Borderline-SMOTE with AIRS algorithm for estimating brain metastasis from lung cancer: A case study in Taiwan
Developing a new classifier approach by combining borderline SMOTE and AIRS. Eight medical datasets from UCI machine learning repository were evaluated. Outperform well-known classifiers. The proposed method can handle imbalanced class problems efficiently. Applying successfully to the prediction of lung cancer to brain metastasis.
brain metastasis lung cancer artificial immune recognition system imbalance dataset borderline-synthetic minority over sampling technique