Application of Unbalanced Data Classification Based on CSD‐ELM in English Network Teaching Mode
Xie Ying-mei · Wireless Communications and Mobile Computing · 2022
In order to accurately predict the frame loss phenomenon, this study proposes a continuous frame loss test for CSD‐ELM short‐range wireless communication. First, the noise and trend direction of CSD‐ELM are removed, the preprocessing of the data is completed, then, these data are injected into the BP neural network as a training sample, continuous frame loss tests are performed on the communication data, the results are output, and the calculation formula is obtained. The test results show that the method has higher accuracy, lower false alarm rate, and shorter test time and is suitable for the detection of continuous data frame loss in short‐distance wireless communication. Then, this study proposes an integrated WELM algorithm, which combines the features of AdaBoost, which can generate strong classifiers through the integration of weak classifiers, so as to achieve the best performance for imbalanced data classification. The algorithm assigns different methods to update the weights of different types of samples, so as not to violate the skewness of the weight distribution. The sigmoid function uses error calculation technology, which can separately reflect the classification characteristics of the two samples, and improves the antinoise function of the algorithm. Finally, this study introduces how to analyze and design a college network English learning management system that can meet the requirements of “network system and classroom English teaching mode” in the internet environment. It solves the time limitation of existing English classrooms, expands college English courses to be student centered, creates interactive online classroom spaces, realizes English classrooms regardless of time and region, avoids teachers’ duplication of work, improves teachers’ teaching efficiency, and ultimately promotes the improvement of students’ learning efficiency.