Reducing Redundancy of Data Samples Method Based on Data-Importance Analysis

With the explosive increase in mobile data volume, traditional cloud platforms can no longer meet the real-time requirements of edge devices. In this context, some scholars have proposed deploying a middle cloud, also known as an edge cloud. The data storage and processing capabilities of the edge cloud are far inferior to those of the center cloud, thus it cannot efficiently handle large-scale edge data samples. Moreover, traditional wireless communication pursues high data rates or reliable transmission, with data bits being transmitted indiscriminately to the network edge, resulting in high redundancy. Therefore, reducing the scale of edge data samples and eliminating high redundant data samples has become an urgent issue to be addressed. This paper mainly focuses on the edge service side and proposes a method for reducing data sample redundancy based on data-importance analysis, which includes three steps: firstly, clustering the original edge data samples to obtain important classes; Secondly, calculating the similarity of samples within each important class; And thirdly, screening the samples. We trained SVM, KNN, and DT classification models for the Iris dataset using the proposed method. The average accuracy of the DT and SVM models has increased by 1.192% and 3.432% respectively, while it has almost no impact on the accuracy of KNN. The experiment shows that removing highly redundant samples can still preserve the features of model training, and even improve the accuracy of the model.