Integrated multi-classifier fusion classification method and integrated multi-classifier fusion classification system based on graph clustering label propagation

Abstract

An integrated multi-classifier fusion classification method based on graph clustering label propagation comprises the following steps: using a training sample to train a basic classifier and clustering the training sample and a testing sample for multiple times to obtain multiple clustering partition states; carrying out label propagation based on the clustering partition states to obtain a clustering category label of the testing sample; processing all the clustering partition states and the basic classifiers according to the above-mentioned steps to obtain a clustering category information set of the testing sample; and making the clustering category information and classification information of the basic classifiers jointly constitute a decision matrix of an integrated classifier, setting parameters of a classification fusion target equation according to the clustering category information and the classification accuracy rate of the classification information of the basic classifiers so as to limit the range of the parameters in fusion, and using a BGCM method to carry out fusion classification on clustering category information of a to-be-classified sample and predicted label information of the basic classifiers according to the classification fusion target equation to obtain a final category label. The integrated multi-classifier fusion classification method is high in classification accuracy rate when difference exists among samples.

Description

technical field [0001] The invention belongs to the field of classification, in particular to an integrated multi-classifier fusion classification method and system based on graph clustering label propagation. Background technique [0002] In the actual classification situation, there are cases where there is a distribution difference between the samples to be classified and the training samples. Due to the clustering error between the samples to be classified and the training samples, common classifiers cannot overcome the differences between samples to achieve accurate classification of the samples to be classified. [0003] In pattern classification, a single classifier generally describes the attributes of samples from a certain angle. When there is a distribution gap between the samples to be classified and the training samples, classification errors are prone to occur. The multi-classifier ensemble classification method refers to the method of combining multiple class...

AI Technical Summary

Problems solved by technology

The classification method combined with the basic classifier and clustering division of the present invention solves the problem of low classification accuracy of existing integrated classifiers when there are differences in samples

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