Adaptive kernel-density approach for imbalanced binary classification
This addresses the critical issue of imbalanced classification in domains like medical diagnosis and anomaly detection, where minority class recognition is essential, though it appears incremental as it extends existing kernel density estimation frameworks.
The paper tackled the problem of class imbalance in binary classification, which biases predictions toward the majority class, by proposing KOTARO, a method that adaptively adjusts decision boundaries using kernel density estimation, and demonstrated that it outperformed conventional methods, especially under severe imbalance conditions.
Class imbalance is a common challenge in real-world binary classification tasks, often leading to predictions biased toward the majority class and reduced recognition of the minority class. This issue is particularly critical in domains such as medical diagnosis and anomaly detection, where correct classification of minority classes is essential. Conventional methods often fail to deliver satisfactory performance when the imbalance ratio is extremely severe. To address this challenge, we propose a novel approach called Kernel-density-Oriented Threshold Adjustment with Regional Optimization (KOTARO), which extends the framework of kernel density estimation (KDE) by adaptively adjusting decision boundaries according to local sample density. In KOTARO, the bandwidth of Gaussian basis functions is dynamically tuned based on the estimated density around each sample, thereby enhancing the classifier's ability to capture minority regions. We validated the effectiveness of KOTARO through experiments on both synthetic and real-world imbalanced datasets. The results demonstrated that KOTARO outperformed conventional methods, particularly under conditions of severe imbalance, highlighting its potential as a promising solution for a wide range of imbalanced classification problems