Defining the subcellular distribution of all human proteins and their remodeling across
cellular states remains a central goal in cell biology. Here, we present a high-resolution …

Dataset imbalances pose a significant challenge to predictive modeling in both medical and
financial domains, where conventional strategies, including resampling and algorithmic …

Uplift modeling is a widely recognized predictive approach used to identify individuals who
are more likely to respond positively to an intervention or treatment, such as a marketing …

In this paper, we propose a novel matching strategy to correct for confounding in uplift
modeling. Our method, called propensity score oversampling and matching (ProSOM) …

Under the background of China's" double carbon" goal, coal mining must become a
systematic project that takes into account both economic strategy and environmental …

Handling imbalanced datasets in deep learning presents a significant challenge, often
resulting in biased model performance. While large models with high parameter counts can …

In the era of big data, technologies like the Internet of Things, smart cities, healthcare, and
social media rely heavily on advanced data analytics. In medical data, certain critical …

Integrating machine learning and data mining is crucial for processing big data and
extracting valuable insights to enhance decision-making. However, imbalanced target …

Imbalanced datasets present a challenging problem in machine learning and artificial
intelligence. Since most models typically assume balanced data distributions, imbalanced …

Imbalanced multiclass datasets pose challenges for machine learning algorithms. These
datasets often contain minority classes that are important for accurate prediction. Existing …