A prediction model based machine learning algorithms with feature selection approaches over imbalanced dataset

Hamoud, Alaa Khalaf; Kamel, Mohammed B. M.; Gaafar, Alaa Sahl; Alasady, Ali Salah; Humadi, Aqeel Majeed; Awadh, Wid Akeel; Dahr, Jasim Mohammed

doi:10.11591/ijeecs.v28.i2.pp1105-1116

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…According to the study's [13] results, AP SMOTE had the highest yield when SMOTE and AP SMOTE were applied to an unbalanced data set. The study's findings showed that class data when students graduated that weren't balanced could be classified with greater accuracy, precision, and sensitivity when the SMOTE method was used [4], [11], [14], [15].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Student Performance Prediction through LSTM-based Deep Learning Models with Unbalanced Data Handling using Oversampling Approach

Ismanto,

Ghani,

Aziz

et al. 2023

Advances in Social Science, Education and Humanities Research

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Accurate prediction of student performance is crucial in learning analytics to prevent course failures and improve academic outcomes. However, publicly accessible educational data often contains noise and imbalanced data distributions, requiring effective handling techniques. In this study, we propose a novel approach that combines the Synthetic Minority Over-sampling Technique (SMOTE) with Long Short-Term Memory (LSTM) and Feed-Forward Neural Network (FFNN) models for performance prediction in virtual learning environments (VLEs). Our experimental results show that utilizing the SMOTE technique significantly improves the accuracy of predicting student withdrawals, with the LSTM model achieving the highest accuracy of 94.90% in the 25th week of data testing. These findings indicate the effectiveness of the SMOTE technique in addressing data imbalance issues in VLE datasets and the potential of our proposed deep learning models in accurately predicting student performance. The implications of our study are significant for learning analytics and educational institutions, as accurate prediction of student performance can inform early interventions and personalized support. Future research could explore the generalizability of our approach in diverse educational contexts and the integration of additional features for further improving prediction accuracy. Hence, our study contributes to the field of learning analytics by proposing a novel approach that combines SMOTE with deep learning models for student performance prediction in VLEs. Our findings highlight the potential of our approach in addressing data imbalance challenges and accurately predicting student performance, with implications for enhancing student success in educational settings.

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Section: Related Workmentioning

confidence: 99%

Enhancing Student Performance Prediction through LSTM-based Deep Learning Models with Unbalanced Data Handling using Oversampling Approach

Ismanto,

Ghani,

Aziz

et al. 2023

Advances in Social Science, Education and Humanities Research

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“…Under-sampling is done by reducing the amount of data from the majority class. Meanwhile, oversampling is done by increasing the data from the minority class [22], [23]. Another approach to dealing with the class unbalanced problem is to use stratified k-fold cross-validation (SCV) as an extension of the cross-validation technique.…”

Section: Introductionmentioning

confidence: 99%

Categorical encoder based performance comparison in pre-processing imbalanced multiclass classification

Yustanti

Iriawan

Irhamah

2023

IJEECS

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The contribution of this study is to offer suggestions for coding techniques for categorical predictor variables and comprehensive test scenarios to obtain significant performance results for imbalanced multiclass classification problems. We modify scenarios in the data mining process with the sample, explore, modify, model, and assess (SEMMA) framework coupled with statistical hypothesis testing to generalize the model performance evaluation conclusions as enhanced-SEMMA. We selected four open-source data sets with unequal class distributions and categorical predictors. Ordinal, nominal, dirichlet, frequency, target, leave one, one hot, dummy, binary, and hashing encoder methods are used. We use the grid-search technique to find the best hyperparameters. The F1-Score and area under the curve (AUC) are evaluated to select the optimal model. In all datasets with 10-fold stratified cross-validation and 95% to 99% accuracy for each dataset, the results show that support vector machine (SVM) outperforms the decision tree (DT) K-nearest neighbor (KNN), Naïve Bayes (NB), logistic regression (LR), and random forest (RF) algorithms. Probability-based or binary encodings, such as target, Dirichlet, dummy, one-hot, or binary, are best for situations with less than 3% of minor class proportions. Nominal or ordinal encoders are preferred for data with a minor class proportion of more than 3%.

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Enhancing algorithmic assessment in education: Equi-fused-data-based SMOTE for balanced learning

Chachoui,

Azizi,

Hotte

et al. 2024

Computers and Education: Artificial Intelligence

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A prediction model based machine learning algorithms with feature selection approaches over imbalanced dataset

Cited by 4 publications

References 36 publications

Enhancing Student Performance Prediction through LSTM-based Deep Learning Models with Unbalanced Data Handling using Oversampling Approach

Enhancing Student Performance Prediction through LSTM-based Deep Learning Models with Unbalanced Data Handling using Oversampling Approach

Categorical encoder based performance comparison in pre-processing imbalanced multiclass classification

Enhancing algorithmic assessment in education: Equi-fused-data-based SMOTE for balanced learning

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