RnkHEU: A Hybrid Feature Selection Method for Predicting Students’ Performance

Xiao, Wen; Hu, Juan

doi:10.1155/2021/1670593

Cited by 6 publications

(4 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Seven different traditional machine-learning algorithms were used to predict the timevarying attention level of students and obtained the moderate accuracy of 75.3%. Xiao et al used hybrid feature selection method RnkHEU that integrates ranking-based forward and heuristic search for predicting the academic performance of students [43]. Different classifiers such as NB, C4.5, MLP, and KNN were used as classifiers and the RnkHEU method improved the classification accuracy by 10% with the highest accuracy being 71.19%.…”

Section: Discussionmentioning

confidence: 99%

“…Amrieh et al used Artificial Neural Networks, Naïve Bayes, and Decision Tree approaches to classify the students' academic performance by using behavioral features and tried to improve the performance by using the ensemble method, which achieved up to a 25.8% improvement [42]. Xiao et al used the hybrid feature selection method RnkHEU that integrates ranking-based forward and heuristic search for predicting the academic performance of students [43]. Different classifiers such as NB, C4.5, MLP, and KNN were used as classifiers and the RnkHEU method improved the classification accuracy by 10% with the highest accuracy being 71.19%.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Feature Extraction Model to Categorize Student Attention Pattern and Its Relationship with Learning

2022

View full text Add to dashboard Cite

The increase of instructional technology, e-learning resources, and online courses has created opportunities for data mining and learning analytics in the pedagogical domain. A large amount of data is obtained from this domain that can be analyzed and interpreted so that educators can understand students’ attention. In a classroom where students have their own computers in front of them, it is important for instructors to understand whether students are paying attention. We collected on- and off-task data to analyze the attention behaviors of students. Educational data mining extracts hidden information from educational records, and we are using it to classify student attention patterns. A hybrid method is used to combine various techniques like classifications, regressions, or feature extraction. In our work, we combined two feature extraction techniques: principal component analysis and linear discriminant analysis. Extracted features are used by a linear and kernel support vector machine (SVM) to classify attention patterns. Classification results are compared with linear and kernel SVM. Our hybrid method achieved the best results in terms of accuracy, precision, recall, F1, and kappa. Also, we correlated attention with learning. Here, learning corresponds to tests and a final course grade. For determining the correlation between grades and attention, Pearson’s correlation coefficient and p-value were used.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Feature Extraction Model to Categorize Student Attention Pattern and Its Relationship with Learning

2022

View full text Add to dashboard Cite

show abstract

“…In the subsequent step, wrapper techniques were used to select the best features. Two of the most crucial algorithms used in this method are random forest importance (RFI) and LASSO regularization (LR) [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Intrusion Detection Efficiency using a Partitioning-based Recursive Feature Elimination in Big Cloud Environment

Elmasry¹,

Khedr²,

Abdelkader³

2023

IJACSA

View full text Add to dashboard Cite

In the era of cloud computing, the effectiveness of utilizing supervised machine-learning-based intrusion detection models for categorizing and detecting malicious network attacks depends on the preparation, extraction, and selection of the optimal subset of features from the dataset. Therefore, before beginning the training phase of the machine learning classifier models, it is required to remove redundant data, manage missing values, extract statistical features from the dataset, and choose the most valuable and appropriate attributes using the Python Jupyter Notebook. In this study, partitioning-based recursive feature elimination (PRFE) method was suggested to decrease the complexity space and training time for machine learning models while increasing the accuracy rate of detecting malicious attacks. On the information security and object technology cloud intrusion dataset (ISOT-CID), some of the most popular supervised machine learning classification techniques, including support vector machines (SVM) and decision trees (DT), have been assessed using the suggested PRFE technique. In comparison to some of the most popular filter and wrapperbased feature selection strategies, the results of the practical experiments demonstrated an improvement in accuracy, recall, F-score, and precision rate after using the PRFE technique on the ISOT-CID dataset. Additionally, the time required to train the machine-learning models was reduced.

show abstract

“…Once data is cleaned, feature selection is applied to educational data. Feature selection consists of choosing the best available attributes based on certain evaluation criteria (Xiao et al, 2021).…”

Section: Student Development Environmentmentioning

confidence: 99%

Using educational data mining to predict sub-Saharan African science, technology, engineering, and mathematics students’ academic performance: a systematic review.

Mhlongo

View full text Add to dashboard Cite

moulded the research idea into reality, and their support and empathy also contributed to the continuation and completion of this research. I also want to thank Ms. Deborah Cunynghame for her continuous assistance and help with the administrative tasks of my research. I also want to thank my family, friends, and colleagues who provided the much-needed moral and technical support. Finally, I want to thank the almighty God for giving me the idea, will, and a constant supply of strength to complete my research.

show abstract

RnkHEU: A Hybrid Feature Selection Method for Predicting Students’ Performance

Cited by 6 publications

References 56 publications

Hybrid Feature Extraction Model to Categorize Student Attention Pattern and Its Relationship with Learning

Hybrid Feature Extraction Model to Categorize Student Attention Pattern and Its Relationship with Learning

Enhancing the Intrusion Detection Efficiency using a Partitioning-based Recursive Feature Elimination in Big Cloud Environment

Using educational data mining to predict sub-Saharan African science, technology, engineering, and mathematics students’ academic performance: a systematic review.

Contact Info

Product

Resources

About