Affiliations: [a]
State Grid Hebei Marketing Service Center, Shijiazhuang, Hebei, China
| [b] State Grid Hebei Electric Power Co., Ltd., Shijiazhuang, Hebei, China
Correspondence:
[*]
Corresponding author. Anlei Liu, State Grid Hebei Marketing Service Center, Shijiazhuang, Hebei, 050000, China. E-mail: anyou5622005847@163.com.
Abstract: In order to ensure the efficiency of power user’s requirements processing, an automatic classification method for demand test of power users based on parallel naive Bayesian algorithm is proposed. Polynomial naive Bayes is selected to build Hadoop cluster, and the feature words of power user’s requirements are selected through chi square test. The weight of each feature item is calculated by word frequency-inverse text frequency index method, and the weight sum of each category is calculated. The weight sum is input into naive Bayes algorithm to output the text classification results of power user’s requirements. At the same time, The naive Bayes classification algorithm is parallelized and encapsulated to reduce the cost of data movement and exchange in the classification process, and improve the operation efficiency of demand text classification of power user. The experimental results show that this method can accurately extract the feature words of power user’s requirements, effectively realize the automatic classification of power user’s requirements text, and have a more accurate classification effect. The average fitness value of the proposed method tends to be stable after more than 20 training times, and the number of network convergence steps is 7. When the ratio of energy function is about 0.4 and 0.6, the average IU value is the highest. When the required number of texts ranges from 500 to 1500, the delay time of text classification is 0.02 s, and the peak signal-to-noise ratio is more than 33, among which the highest peak signal-to-noise ratio is 42.52, and the normalization coefficient is 1.
Keywords: MapReduce, Naive Bayes, power user’s requirements, automatic text classification, parallel processing
