Anomaly identification of English online learning data based on local outlier factor
by Yuying Liu
International Journal of Computer Applications in Technology (IJCAT), Vol. 73, No. 4, 2023

Abstract: In order to solve the problems of low recognition accuracy, low-recall rate and low absolute value of F1-score in traditional online English learning data anomaly identification methods, a new anomaly identification of English online learning data based on local outlier factor is proposed. English online learning data resources are utilised to be mined using K-nearest neighbours and local reachability density, and a unified data set is created by integrating data from different sources and formats. The information source model of the data set is abstracted as a tuple, and English online learning data anomaly identification is achieved by implementing the local outlier factor threshold in the tuple. The experimental results show that the proposed method has an identification accuracy of over 90%, a maximum recall rate of 98% and a high absolute F1-score, indicating good identification performance.

Online publication date: Fri, 31-May-2024

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