Application of Improved Multi-class Active Learning Algorithm in Data Processing

Abstract

In the context of intelligent customer service in the electric power industry, in order to effectively classify customer data in a reasonable and correct manner, greatly improve the analysis effect of big data, and provide accurate customer service, a multi-category Active Learning algorithm based on multiple Clustering algorithms and multivariate Linear regression (ALCL) algorithm was proposed. Firstly, the category matrix corresponding to each algorithm was obtained through multiple heterogeneous clustering algorithms, the category matrix was labeled and pre-classified by querying common points. Secondly, the key examples used to train the weight coefficient model of the clustering algorithm were selected through the largest search strategy and the most confusing query strategy. Thirdly, the objective solving function was defined, the weight coefficients of each clustering algorithm were obtained by training key examples. Finally, samples with high confidence in the results were classified by the classification calculation combined with the weight coefficient. Six public customer data sets in Yuzhong District, Chongqing were used for experiments. The experimental results show that when the classification accuracy of ALCL is the highest, it is improved by 2.07%~14.01% compared with traditional supervised learning algorithms and other active learning algorithms. The results of hypothesis and significance analysis prove that ALCL has better classification effect in customer data processing.