.png){kind=icon}
返回
Enhanced binary genetic algorithm as a feature selection to predict student performance
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-021-06424-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Students' performance prediction systems play a vital role in enhancing the educational performance inside universities, schools, and training centers. Big data can come from different resources such as examination centers, virtual courses, registration departments, e-learning systems. Extracting meaningful knowledge from educational data is a complex task, so reducing the data dimensionality is needed. In this paper, we proposed an enhanced binary genetic algorithm (EBGA) as a wrapper feature selection algorithm. Novel hybrid selection mechanism based on a k-means algorithm and electromagnetic-like mechanism (EM) method is proposed. K-means will cluster the population into a set of clusters, while EM will determine a value called a total force (TF) for each solution. Each cluster has an accumulated total force (ATF) (i.e., adding all TFs together). Selection process will select two solutions with the highest TF from the cluster, which has the highest ATF. We employed a hybrid machine learning approach between the proposed EBGA and five different classifiers (i.e., k-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA)). Two real case studies obtained from UCI Machine Learning Repository are used in this paper. Obtained results showed the ability of the proposed approach to enhance the performance of the binary genetic algorithm. Moreover, the performances of all classifiers are improved between 1% and 11%.
Keyword:
Genetic algorithm
Feature selection
Students performance
Electromagnetic-like mechanism
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
分享收藏Genome sequence analysis of a novel Enterobacter cloacae phage, Ec_L1, belonging to the genus Eclunavirus一株新型阴沟肠杆菌噬菌体Ec_L1（属于Eclunavirus属）的基因组序列分析
PREAI
分享收藏Neural-fuzzy with representative sets for prediction of student performance.png)
APPLIED INTELLIGENCE
OAAI
APPLIED INTELLIGENCEIF3.5
OAAI分享收藏Data mining for providing a personalized learning path in creativity: An application of decision treesCOMPUTERS & EDUCATION
COMPUTERS & EDUCATIONIF10.5
PREAI
分享收藏Clustering blended learning courses by online behavior data: A case study in a Korean higher education institute通过在线行为数据对混合学习课程进行聚类: 以韩国高等教育机构为例
PREAI
分享收藏
查看更多内容