Data Preprocessing, Feature engineering, Machine learning, Deep learning
Abstract
Data preprocessing has a vital impact on the performance of traditional machine learning. However, with the continuous development of deep learning technology and the powerful representation learning ability of neural networks, deep learning models can easily convert raw data into continuous feature representation and have made remarkable achievements in many downstream tasks. Recently, with the application of deep learning technology in more fields that require robustness and stability, its model bias caused by data quality problems are gradually exposed, which makes the data preprocessing technology regain the attention of researchers. This paper systematically expounds on the primary data preprocessing technology in machine learning and discusses the model bias caused by data deviation and the robustness in the face of attacks. In addition, this paper also illustrates the possibility of a data preprocessing foundation in solving the defects of deep learning foundation based on glove word vector and image classification task based on convolutional neural network. The research in this paper can provide a valuable reference for researchers in related fields.
