期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2019
卷号：97
期号：1
页码：279-289
出版社：Journal of Theoretical and Applied
摘要：Educational Data Mining (EMD) is in charge of discovering useful information from educational datasets. In recent years, the data is mounting rapidly due to the ease access to the websites of e-learning intakes extraordinary enthusiasm from different colleges and instructive foundation. High dimensionality, irrelevant, redundant and noisy dataset can affect the knowledge discovery during the training phase in a bad way as well as degrading machine learning performance accuracy. All these factors often rise demand for dataset preparation, analysis, and feature selection. The fundamental aim of research is to enhance the precision of classification by information preprocessing and expel the unessential information without discarding any vital data by means of feature selection.This paper proposes EDM dataset preprocessing, and hybrid feature selection method by combining filter and wrappers techniques. In the filter-based feature selection, the statistical analysis is based on the Pearson correlation and information gain. In the wrapper method, the accuracy of the feature subset is tested using a neural network as a baseline algorithm. The obtained results show an enhancement in performance accuracy toward selecting minimum feature subset with high predictive power over using all features.
关键词：Educational Data Mining; Hybrid Feature Selection; Neural Network; Data Preprocessing; Accuracy.
