Multi-label classification method and system, readable storage medium and computer equipment

Abstract

The invention provides a multi-label classification method and system, a readable storage medium and computer equipment, and the method comprises the steps: converting a multi-label classification problem into two types of classification problems, and enabling each two types of classifiers to correspond to one label in a multi-label data set; Constructing a semi-supervised multi-mark learning model according to preset feature selection and the correlation between the two types of classifiers; Solving the semi-supervised multi-label learning model by adopting a preset algorithm to obtain a semi-supervised multi-label classification model parameter and a label correlation matrix; And predicting a mark set to which the unknown mark belongs according to the model parameters and the mark correlation matrix. In the multi-label classification method, a semi-supervised multi-label learning method combining label correlation and feature selection is adopted, a large number of unlabeled multi-label samples are effectively utilized, correlation between labels is automatically obtained through learning, in addition, dimensionality reduction is conducted on high-dimensional data, and a multi-label classifier with the better generalization performance can be obtained.

Description

technical field [0001] The present invention relates to the technical field of multi-label learning, in particular to a multi-label classification method, system, readable storage medium and computer equipment. Background technique [0002] Machine learning is an important research field in computer science, which studies how computers simulate or realize human learning behavior. Supervised learning is the most studied and widely used learning framework in machine learning, which assumes that each learning object belongs to only one label. [0003] But in the real world, one token is often not enough to accurately describe some complex semantic objects. It can be seen that complex learning objects with multiple labels at the same time are ubiquitous, and traditional supervised learning methods are difficult to deal with these complex learning objects well. [0004] For this reason, multi-label learning came into being, and its task is to learn the known multi-label data se...

AI Technical Summary

Problems solved by technology

[0006] Based on this, the object of the present invention is to provide a multi-label classification method, system, readable storage medium and computer equipment, t

Images