An Optimized Hybrid Fuzzy Weighted k-Nearest Neighbor with the Presence of Data Imbalance

We present an optimized hybrid fuzzy Weighted k-Nearest Neighbor classification model in the presence of imbalanced data. More attention is placed on data points in the boundary area between two classes. Finding greater results in the general classification of imbalanced data for both the minority and the majority classes. The fuzzy weighted approach assigns large weights to small classes and small weights to large classes. It improves the classification performance for the minority class. Experimental results show a higher average performance than other relevant algorithms, e.g., the variants of kNN with SMOTE such as Weighted kNN alone and Fuzzy kNN alone. The results also signify that the proposed approach makes the overall solution more robust. At the same time, the overall classification performance on the complete dataset is also increased, thereby improving the overall solution.