Complex Data Prediction and Classification Methods based on Feature Selection and Data Dimensionality Reduction

Data prediction and classification is a very important research topic in the field of data mining, which has been a wide concern for a long time. Data prediction and classification theory is the crystallization of the intersection and integration of different disciplines such as management science, economics, mathematics, and computer, and has been widely used in many fields, such as energy price market analysis, financial market price prediction, and risk control, biological information recognition, business intelligence customer behavior analysis and so on. In recent years, with the rapid development of information technology and Internet technology, and the gradual maturity of cloud computing and big data analysis, the research on complex data prediction and classification is full of opportunities and challenges. This paper focuses on three aspects: the dimensional complexity of data, the complexity of frequency domain characteristics, and the identification and matching of multi-source information, and closely focuses on the key issues of data prediction and classification Electricity market price data is the main research object and case, and the prediction and classification of complex data are studied.