随机森林算法下智慧课堂多维度数据挖掘研究

Research on Multi-Dimensional Data Mining in Smart Classrooms under Random Forest Algorithm

针对智慧课堂中由于多维度数据特征复杂导致数据挖掘难度增加的问题,为提高数据挖掘的查全率、查准率和挖掘效率,提出了一种基于随机森林算法的智慧课堂多维度数据挖掘方法。本方法将云平台、教务平台以及其它平台作为智慧课堂多维度数据来源并采集相关数据,同时对采集的数据进行预处理,剔除异常与相似度程度高的数据。根据数据预处理结果与卡方检验原理,从数据中选择与智慧课堂相关的多个维度特征,并根据所选择的特征与随机森林开展多维度数据挖掘。实验结果表明,所提方法表现出了较好的性能,能够显著改善数据挖掘的准确性和可靠性。

To address the increased difficulty in data mining caused by complex multi-dimensional data features in smart classrooms,and to improve the recall rate,precision,and efficiency of data mining,a multi-dimensional data mining method for smart classrooms based on the Random Forest algorithm is proposed.The method utilizes cloud platforms,educational administration platforms,and other platforms as sources for multi-dimensional data in smart classrooms,collecting relevant data and preprocessing it to eliminate anomalies and highly similar data.Based on the preprocessing results and chi-square test principles,multiple dimensional features related to smart classrooms are selected from the data.Multi-dimensional data mining is then conducted using the selected features and the Random Forest algorithm.Experimental results demonstrate that the proposed method exhibits good performance,significantly improving the accuracy and reliability of data mining.