[1]任雪妮,罗幼喜. 基于bagging算法的经济金融数据分析[J].湖北工业大学学报,2021,(2):110-114.
　REN Xueni,LUO Youxi. Research and Analysis of Economic and Financial Data Based on Bagging Algorithm[J].,2021,(2):110-114.
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 基于bagging算法的经济金融数据分析()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2021年第2期
页码:
110-114
栏目:
湖北工业大学学报
出版日期:
2021-04-22

文章信息/Info

Title:
 Research and Analysis of Economic and Financial Data Based on Bagging Algorithm
文章编号:
1003-4684(2021)02-0110-05
作者:
 任雪妮;  罗幼喜
 湖北工业大学理学院， 湖北 武汉 430068
Author(s):
 REN Xueni;  LUO Youxi
 School of Sciences, Hubei Univ. of Tech., Wuhan 430068, China
关键词:
 ; bagging算法; C5.0决策树算法; KNN算法; 朴素贝叶斯算法; 经济金融数据
Keywords:
 ; bagging algorithm;  C5.0 decision tree algorithm;  KNN algorithm;  Naive bayes algorithm; Economic and financial data
分类号:
F22
文献标志码:
A
摘要:
 为了研究弱分类器的个数及种类的构成对强分类器预测准确性的影响，以及在不同经济金融数据的预测情况，选择credit，bank，stock，audit数据集模拟bagging算法分类器的个数和分类器构成方法不同时的预测情况。其中由KNN算法、C5.0算法和朴素贝叶斯算法构造了7种不同的组合方法。总的来看，基于bagging的C5.0决策树方法并设置分类器个数为50时预测效果最佳。对bank数据集设置最佳的方法和分类器个数进行实例分析，准确性达到94.17%，预测情况良好。
Abstract:
 In order to study the influence of the number and type composition of weak classifiers on the accuracy of strong classifier prediction and the prediction of different economic and financial data, the data sets of Credit, Bank, Stock and Audit are selected to simulate the situation where the number of classifiers and the composition of classifiers of Bagging algorithm. Among them, 7 different combination methods are constructed by KNN algorithm, C5.0 algorithm and Naive Bayes algorithm. In general, the C5.0 decision tree method based on Bagging has the best prediction effect when the number of classifiers is set to 50. Finally, an example analysis of the Bank data set shows that the accuracy reaches 94.17% and the prediction is good.

参考文献/References:

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备注/Memo

备注/Memo:
 ［收稿日期］ 2020-10-09
［基金项目］ 国家社会科学基金项目（17BJY210）
［第一作者］ 任雪妮(1996-)， 女，湖北仙桃人，湖北工业大学硕士研究生，研究方向为数据科学与决策
［通信作者］ 罗幼喜(1979-)， 男，湖北黄冈人，理学博士，湖北工业大学教授，研究方向为计量经济建模,数据挖掘
更新日期/Last Update: 2021-04-23