Hybrid model-based unbalanced data classification method and device, equipment and medium

By combining the advantages of improvement algorithm, twin support vector machine and cascade model, the Cascade-Boost-TwinSVM method is formed, which solves the problem of performance degradation of traditional data classification methods on unbalanced data sets, and achieves efficient and stable recognition of rare class and improvement of overall classification effect.

CN119830213APending Publication Date: 2025-04-15CHINA RAILWAY MAJOR BRIDGE RECONNAISSANCE & DESIGN INSTITUTE CO LTD
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Patent Information

Application Number
CN202411903929.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-23
Publication Date
2025-04-15

AI Technical Summary

Technical Problem

When traditional data classification methods deal with unbalanced data sets, the classifier performance has been greatly reduced, the recognition rate of rare classes is very low, the calculation complexity is high, and the efficiency is low.

Method used

The unbalanced data classification method based on hybrid model is adopted, combined with the advantages of the improvement algorithm (AdaBoost), twin support vector machine (Twin-SVM) and cascade model (Cascade Model), and Cascade Model, to form the Cascade-Boost-TwinSVM (CBT) method. Through double-layer iteration, outer cascade model iteration, inner layer introduces twin support vector machines as weak classifiers, form Boost-TwinSVM algorithm, and embeds it into the nodes of each layer of the cascade model, alternately trains to update the sample weights, gradually form component classifiers, and finally integrate them into the final classifier.

Benefits of technology

It improves the classification accuracy of the unbalanced data set, significantly improves the recognition rate of rare classes, reduces the computational complexity, and improves classification efficiency. It is suitable for processing large-scale unbalanced data.

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Abstract

The invention relates to an unbalanced data classification method based on a hybrid model. The method comprises the steps of obtaining a to-be-classified unbalanced data set; calling a twin support vector machine algorithm to form a hybrid model combination algorithm, and performing inner layer iteration; a classification node of the cascade model receives the sample set, a hybrid model combinatorial algorithm allocates an initial weight, and a pre-training set is generated according to the initial weight for training to obtain a component classifier; calculating the AUC value of the component classifier on the pre-selected training set, updating the weight distribution of the sample by taking the AUC value as a reference until the training is completed, and ending the inner layer iteration to obtain the component classifier; integrating all component classifiers into a node classifier to obtain a next sample set, transmitting the next sample set to a next classification node, and performing circulation to obtain the node classifier until the classification node circulation of the cascade model is finished; and integrating the node classifiers into a final classifier, and classifying the to-be-classified unbalanced data set to obtain a classification result. The unbalanced data classification method provided by the invention is high in calculation precision and stable in performance.
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