The Research Of Support Vector Machine Algorithm And The Application In Meteorological Data Mining

This paper proposes a method for weather forecasting of multi-dimensional, time-series meteorological data based on SVM algorithm, which could effectively solve the troublesome problems of meteorological data mining, as the attributes of meteorological data have the characters of multi-dimension, complex, high dependency and correlation, also concerning with time and space factors. The key point to obtain the satisfactory accuracy and effectiveness of meteorological data mining is to effectively select and correctly process the forecasting factors. This paper proposes an approach to compensate the imbalance of classes and to reconstruct training sample set for SVM, which could solve the bias problem of the classification algorithms. With the cost factor of noise data embedded into SVR model, the SVR algorithm obtains the effective regression result in solving problem of overfitting. This paper researches and discusses on the basic methods and principles of selecting factors, processing and classification of large-scale data, reduction and optimization of high dimensional quantization factors, selection and optimization of SVM kernel function's parameters, and the incremental learning SVM algorithm. The experiments have showed that meteorological data mining based on SVM has satisfactory performance and efficiency.