目前越来越多的数据挖掘方法被用于风险预警中,决策树、支持向量机、神经网络、Logistic回归等方法在风险预警中都表现出了较好的特性和预警效果,但是不同数据挖掘分类方法得到的结果不同,往往导致预警结果的不一致,因此也会存在一定风险。本文引入信息融合技术对不同数据挖掘分类方法得到的结果进行融合处理得到最优的结果,解决了不同数据挖掘方法得到的结果不一致问题。文章在SVM和Logistic回归的数据挖掘模型基础上建立基于信息融合的公司财务预警模型,提高了财务预警准确率,并且保留了原数据挖掘方法在分类预测上的优势。在实证研究中,论文选取了中国制造业的上市公司作为研究对象,在SVM和Logistic回归两种数据挖掘模型的基础上利用信息融合方法建立了财务预警模型,实证结果表明,基于信息融合的数据挖掘方法的预测准确率要高于单独的SVM和Logistic回归两种方法。
Different data mining methods for classification can produce different results. However, "one time" data mining process cannot often obtain a well support decision, so we introduce information fusion technique to fuse the different results to gain an optimal solution. In this paper, information fusion technique is used to build a finance early-warning model based on data mining methods such as SVM and Logistic model, 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. The real dataset of Chinese listed manufacturing companies is selected to predict the finance risk with information fusion technique based on SVM and Logistic model, and a higher prediction accuracy than those of the two methods respectively is obtained.
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