Based on Information Fusion Technique with Data Mining in the Application of Finance Early-Warning

Data mining has been widely applied to make prediction for finance crisis risk, and they often obtain a good result. Financial distress prediction can be formulated as a classification problem using data mining. Many data mining methods for classification can be used to solve the finance early-warning problem, however, “one time” data mining process cannot often obtain a well support decision, and one single method has its weakness for classification. In this paper, we use information fusion technique to build a finance early-warning model based on data mining methods, which can integrate the respective strengths from different data mining methods to improve the prediction accuracy rate, it fuses the different data mining results to gain the prediction results for reliable decision. We also choose the real dataset of Chinese listed manufacturing companies to predict the finance risk with information fusion technique based on SVM and Logistic model, and make comparison with the two methods to make prediction respectively.