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

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。

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.