Application of Active Learning Strategies in Fault Detection and Diagnosis within Communication Networks

As wireless networks become extensively utilized in modern society, network stability and service quality have emerged as critical issues. Network failure not only affects the user experience, but may also cause significant economic losses to operators. Therefore, it is particularly important to design efficient fault detection and diagnosis methods. This study introduces a novel framework for network fault detection and diagnosis—a communication network fault prediction method based on optimized support vector machine (SVM) and active learning. The framework incorporates clustering to reinforce the memorization of fault data features and combines it with active learning for data ranking. Based on this ranking, balance weights are adjusted to construct a balanced dataset for wireless communication faults. Finally, the balanced dataset is fed into an SVM model with a base classifier for data classification. Through experiments simulating real-world scenarios, the proposed model has been validated to significantly enhance the efficiency and accuracy of fault detection in a simulated wireless communication network environment. It demonstrates the potential to achieve favorable results in practical applications.