基于一种新的特征选择方法的朴素贝叶斯分类器选择证券的研究
Research on Security Selection by Naive Bayes Classifier Based on a New Feature Selection Method
摘要: 本文提出了基于一种新的特征选择方法的朴素贝叶斯证券分类模型。首先，根据深交所50家公司2011年的交易数据和常用的18个指标，采取新的特征选择方法即互信息和主成分分析相结合选出用于分类的因子；其次，利用前10个月的数据建立朴素贝叶斯分类模型，用后两个月的数据检验模型的预测精度。实证分析表明模型的分类平均正确率达到75%，具有应用价值。
Abstract: In this paper, a naive Bayes classifier for securities selection based on a new feature selection method is established. Firstly, in consideration of the trading data of 50 companies in Shenzhen Stock Exchange and 18 commonly used indicators, a new feature selection method, i.e. the combi-nation of mutual information and principal component analysis, is adopted to select the value fac-tors for classification. Secondly, a naive Bayes classifier is constructed with the data of the first 10 months, and the prediction accuracy of the classifier is tested with that of the last two months. The empirical analysis shows that the average accuracy of the classifier reaches 75%, which is of ap-plication value.
文章引用：郭盼盼, 刘海军, 李双双. 基于一种新的特征选择方法的朴素贝叶斯分类器选择证券的研究[J]. 应用数学进展, 2019, 8(1): 41-49. https://doi.org/10.12677/AAM.2019.81005

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