文章基本信息

标题：Incorporating Feature Selection in the Improved Stacking Algorithm for Online Learning Analysis and Prediction
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作者：Hong Dai ; Wenkai Wu ; Jiacheng Li 等
期刊名称：Engineering Letters
印刷版ISSN：1816-093X
电子版ISSN：1816-0948
出版年度：2020
卷号：28
期号：4
页码：1011-1022
语种：English
出版社：Newswood Ltd
摘要：Online learning is becoming a common learning method in the field of education. The correct classification of online learners plays a vital role in solving the key issues such as low pass rates and high dropout rates. In this paper, we propose a improved ensemble algorithm for classifying learners, which integrates feature selection and the improved Stacking algorithm (Stacking-PMLR). One feature selection algorithm is Mean Decrease Impurity Algorithm based on Random Forest. It is used to investigate the learning behavior factors which contribute to class of learner. It is also used to select the most frequent features and to reduce the dimensions.   Analyzing learners’ behavior features by the feature selection algorithm, we know that a number of chapters, interaction days, interactions times and video viewed times are the most important factors. Learners’ behavior features from feature selection are used as the attribute input of Stacking-PMLR for classifying learners. After that, we use the multilevel improved ensemble algorithm Stacking-PMLR to classify learners. We improve the Stacking algorithms in terms of its hierarchical structure, data features representation, combination strategy and classification algorithm according to its own characteristics. We use the improved Stacking algorithm to construct the classification model. In addition, fifteen real world different type datasets in UCI machine learning repository are applied. The experimental results show that the improved Stacking algorithm has better performance in accuracy, precision and F 1. It also shows the feasibility of the Stacking-PMLR. Finally, we use feature selection and the Stacking-PMLR algorithm to classify the public dataset of the edX online learning platform. The experimental results show that the performance of Stacking-PMLR is better. It shows the practical value of the Stacking-PMLR in online learning prediction.
关键词：Online learning; ensemble algorithm; feature selection; stacking algorithm