IT Operation and Maintenance Data Classification Method Integrating Data Augmentation and Ensamble Learning
The rapid development of artificial intelligence for IT operations generated a huge demand for the automatic classifi-cation of IT operation and maintenance data.The text classification method based on deep learning has achieved better results than the traditional machine learning method.However,the text classification of unbalanced data sets still faces challenges,a single neu-ral network model can not extract and synthesize the multi-dimensional information in the text.In view of this,the paper proposes an IT operation and maintenance data classification method integrated data enhancement and ensamble learning.This method pro-poses a text data augmentation method based on TF-IDF keyword extraction algorithm,and a relatively balanced training data set is obtained by text data enhancement of small sample categories.For the more,TextCNN,TextRCNN and FastText are used as the base classifiers for training and prediction respectively.The obtained probability is integrated by the softvoting method to obtain the IT operation and maintenance data classification model.Theoretical analysis and experimental results show that compared with tradi-tional classification methods,this classification method effectively solves the problem of data imbalance and achieves better classifi-cation results.
text classificationartificial intelligence for IT operationsdata augmentationdeep learningensamble learning
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