Research on Feature Optimization Scheme Based on Data Feature Enhancement

Based on the common knowledge in the field of feature engineering that “data and features determine the upper limit of the model, and models and algorithms just approach the upper limit”, this article believes that the knowledge of feature engineering can be used to extract features from the original data to the maximum. Used by algorithms and models to improve the accuracy of algorithms and models. Based on the above considerations, this paper proposes a feature optimization scheme based on data feature enhancement (DFET) to improve the performance of deep learning models in natural language processing. The DFET feature optimization scheme uses machine learning classification algorithms to weaken noise features and adopts a sliding window Data enhancement is performed in the form of a window, the ignored features are selected through the form of windows, and low-dimensional data with significant features are generated. This part of the data is added to the original model training to fine-tune the model training, and then optimize the model to meet the requirements of improving accuracy. The paper examines the security bug report prediction problem in the field of natural language processing to verify the impact of data feature optimization on the performance optimization of deep learning models. Experiments show that compared with the basic deep learning model, the model after the DFET feature optimization scheme can show more excellent feature learning ability in text classification and prediction.