Sample type classification method and device based on multi-dimensional data, equipment and medium

Abstract

This invention discloses a method, apparatus, device, and medium for sample type classification based on multi-dimensional data. The method includes acquiring an initial dataset; binning the data in each dimension to generate an N-dimensional grid structure; performing K-1 region search steps to progressively divide the grid cells in the N-dimensional grid structure into K different type regions, where K is a positive integer greater than 1; and setting a corresponding sample type label for each type region to generate a first type classification result. This method possesses automated fitting and optimization capabilities, supports feature inputs of arbitrary dimensions and binning granularity, and completes type classification and model construction without manual intervention, thereby effectively improving recognition accuracy and output quality while significantly reducing labor costs and substantially improving modeling and iteration efficiency.

Images