《计算机应用研究》|Application Research of Computers

基于BP和朴素贝叶斯的时间序列分类模型

Time series classification model based on BP and naive Bayes

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作者 王会青,郭芷榕,白莹莹
机构 太原理工大学 信息与计算机学院,山西 晋中 030600
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文章编号 1001-3695(2019)08-006-2271-04
DOI 10.19734/j.issn.1001-3695.2018.02.0081
摘要 针对传统时间序列分类方法需要较为繁琐的特征抽取工作以及在只有少量标记数据时分类效果不佳的问题,通过分析BP神经网络和朴素贝叶斯分类器的特点,提出一种基于BP和朴素贝叶斯的时间序列分类模型。利用BP神经网络非线性映射能力和朴素贝叶斯分类器在少量标记数据下的分类能力,将BP神经网络抽取到的特征输入到朴素贝叶斯分类器中,可以较为有效地解决传统时间序列分类算法的问题。实验结果表明,该模型在标记数据较少情况下的时间序列分类中具有较高的分类准确度。
关键词 时序序列; BP神经网络; 朴素贝叶斯; 特征抽取
基金项目 山西省科技攻关项目(201603D221037-2)
国家青年科学基金资助项目(61503272)
本文URL http://www.arocmag.com/article/01-2019-08-006.html
英文标题 Time series classification model based on BP and naive Bayes
作者英文名 Wang Huiqing, Guo Zhirong, Bai Yingying
机构英文名 College of Information & Computer,Taiyuan University of Technology,Jinzhong Shanxi 030600,China
英文摘要 For the low accuracy of classification caused by the lack of labeled data, and the problem of tedious feature extraction of the traditional time series classification method, this paper analyzed the characteristics of BP neural network and naive Bayes classifier, it proposed a method based on BP and naive Bayes. It used the nonlinear mapping ability of BP neural network and the classification ability of naive Bayes classifier under a small amount of labeled data, it input into the features extracted from BP neural network naive Bayes classifier, which could solve the problem of traditional time series classification algorithm. Experimental results show that this model has higher classification accuracy in the classification of time series with fewer labeled data.
英文关键词 time series; BP neural network; naive Bayes; feature extraction
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收稿日期 2018/2/3
修回日期 2018/3/28
页码 2271-2274,2278
中图分类号 TP393
文献标志码 A