Weighted nearest neighbors and radius oversampling for imbalanced data classification

The challenges associated with high-dimensional and imbalanced datasets were observed to often lead to a degradation in the performance of classical machine learning algorithms. In the case of high dimensional data, not all features contribute significantly and are considered relevant to the performance of the model. Therefore, this study introduced a novel method called feature weighted variance analysis-nearest neighbors (WFVANN) which was developed on the foundation of k-nearest neighbors (KNN). The process involved modifying the calculation of the Euclidean distance by fully considering the relevance and contribution levels of features based on their Fvalue. WFVANN at the algorithmic level processing and radius-synthetic minority oversampling technique (R-SMOTE) at the data level processing used as the oversampling method later became the proposed model to solve the aforementioned issues. Moreover, extensive experiments were conducted on two distinct types of data including the high-dimensional and imbalanced by comparing WFVANN with the state-of-art KNN-based and synthetic minority oversampling technique (SMOTE)-based methods. The results showed that the proposed method had the highest accuracy, precision, recall, and F1-measure values across the majority of test datasets and outperformed the other methods.