大型教学系统中的智能大数据关键特征估计方法

A key feature estimation method for intelligent big data in large-scale teaching system

传统二阶特征估计法在对大数据方差进行估计,预测大型教学系统中的智能大数据关键特征时,存在对多特征的智能大数据关键特征估计效果不明显,估计结果误差累计量大的问题。因此,提出大型教学系统的智能大数据关键特征估计方法,其采用Relief关键特征估计方法获取大数据特征权重,完成智能大数据特征流行学习,通过对特征权重选择后的数据空间进行无监督学习和低维嵌入,实现对多特征的智慧大数据的特征估计。基于大数据关键特征估计结果,采用滚动时间序列估计方法,通过AR(p)模型运算大数据特征的模型阶数,依据该阶数向滚动AR算法引入实时数据,解决大数据特征估计中估计结果不同步造成的累计误差问题,实现智能大数据关键特征准确预测。实验结果表明,所提方法可增强对关键特征的估计精度,对关键特征的估计效果也有所提高。

The traditional two-order feature estimation method has the problems of unobvious key feature evaluation effect of multi-feature intelligent big data and big error accumulation quantity of evaluation results when it is used to estimate the variance of big data and predict the key features of intelligent big data in the large-scale teaching system. Therefore,a key feature estimation method for intelligent big data in the large-scale teaching system is proposed. The weights of big data features are obtained by using the key feature estimation method Relief to accomplish the popular learning of intelligent big data features. The unsupervised learning and low-dimensional embedding are performed for data space after feature weight selection,so as to realize the feature estimation of multi-feature intelligent big data. On the basis of the key feature estimation results of big data,the model order of big data features is calculated by using the rolling time series estimation method and AR（ p） model. According to the order,real-time data is introduced to the rolling AR algorithm to resolve the accumulated error problem caused by unsynchronization of evaluation results in big data feature evaluation,so that accurate key feature prediction of intelligent big data can be realized. The experimental results show that the proposed method can improve the estimation precision and effect of key features.