binningbased
binningbased refers to a class of methods and approaches that rely on discretizing continuous data by partitioning the value range into a set of bins and using the resulting discrete representation for analysis, modeling, or decision making. This approach is common across statistics, signal processing, and machine learning, where it serves to simplify computations, reduce noise, or create interpretable features.
The core idea in binningbased methods is to define bin edges and map each data point to
Applications of binningbased methods span data preprocessing for machine learning, image and signal processing, time-series segmentation,