Stacking density estimation and its oversampling method for continuously imbalanced data in chemometrics

Continuously imbalanced data means that the target variable is continuous and its distribution This kind of data is widespread in many practical application areas. However, methods to handle continuously imbalanced data have been relatively scarce, and there is an urgent need corresponding imbalance regression methods to enhance the capability of handling continuously data. Firstly, we propose a Stacking-based density estimation (SDE) method to solve the density problem of continuously imbalanced target variables. SDE links density estimation with the Ensemble algorithm called Stacking, and its core concept is the "fusion of multiple perspectives for accurate Performing SDE enhances the model's understanding of complex data structures and makes it more and accurate in identifying rare values. Subsequently, we investigate an SDE-based oversampling (SDE-OS). SDE-OS uses SDE to synthesize new rare instances in the rare-value region, achieving customization of rare-value additions. In a series of numerical experiments, SDE has been estimated accurately than the kernel density estimation method on ANLL. SDE-OS outperforms conventional methods such as SMOGN and SMOTER in various metrics. Therefore, the proposed SDE and SDE-OS competitive and effective tools for addressing the imbalanced regression problem.