An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery

Siddiqa, Ayesha; Naqvi, Syed Abbas Zilqurnain; Ahsan, Muhammad; Ditta, Allah; Alquhayz, Hani; Khan, Muhammad Adnan

doi:10.32604/cmc.2022.021652

Computers, Materials &Amp; Continua

2022

DOI: 10.32604/cmc.2022.021652

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An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery

Ayesha Siddiqa¹,

Syed Abbas Zilqurnain Naqvi²,

Muhammad Ahsan³

et al.

Abstract: Recent advancements in computer technologies for data processing, collection, and storage have offered several chances to improve the abilities in production, services, communication, and researches. Data mining (DM) is an interdisciplinary field commonly used to extract useful patterns from the data. At the same time, educational data mining (EDM) is a kind of DM concept, which finds use in educational sector. Recently, artificial intelligence (AI) techniques can be used for mining a large amount of data. At … Show more

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Cited by 5 publications

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Analysis and Prediction of Students’ Academic Performance Based on Educational Data Mining

Feng

Fan

2022

IEEE Access

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The development of intelligent technologies gains popularity in the education field. The rapid growth of educational data indicates traditional processing methods may have limitations and distortion. Therefore, reconstructing the research technology of data mining in the education field has become increasingly prominent. In order to avoid unreasonable evaluation results and monitor the students' future performance in advance, this paper comprehensively uses the relevant theories of clustering, discrimination and convolution neural network to analyze and predict students' academic performance. Firstly, this paper proposes that the clustering-number determination is optimized by using a statistic which has never been used in the algorithm of K-means. Then, the clustering effect of K-means algorithm is tested by discriminant analysis. The convolutional neural network is introduced for training and testing data that are labeled with categories. The generated model can be used to predict prospective performance. Finally, in order to validate the prediction results, the effectiveness of the generated model is evaluated by using two metrics in two cross-validation methods. The experimental result demonstrates that the statistic not only solves the difficulty to determine the clustering number in K-means algorithm from an objective and quantitative point of view, but also improves the reliability of prediction results.

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Analysis and Prediction of Students’ Academic Performance Based on Educational Data Mining

Feng

Fan

2022

IEEE Access

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An Adaptive Feature Selection Algorithm for Student Performance Prediction

Roy,

Farid

2024

IEEE Access

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Educational Data Mining (EDM) is used to ameliorate the teaching and learning process by analyzing and classifying data that can be applied to predict the students' academic performance, and students' dropout rate, as well as instructors' performance. The prediction of student performance is complicated by the vast and diverse range of variables from academic records to behavioral and health metrics. In this paper, we have introduced a new Adaptive Feature Selection Algorithm (AFSA) by amalgamating an ensemble approach for initial feature ranking with normalized mean ranking from five distinct methods to enhance robustness. The proposed method iteratively selects the best features by adjusting its threshold based on each feature's rank to ensure significant contributions to model accuracy and also effectively reduces dataset complexity. We have tested the performance of the proposed feature selection algorithm using five machine learning classifiers: Logistic Regression (LR), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes (NB) classifier, and Decision Tree (DT) classifier on four student performance datasets. The experimental results highlight the proposed method significantly decreases feature count by an average feature reduction factor of 5.7, significantly streamlining datasets while maintaining competitive cross-validation accuracy, marking it as a valuable tool in the field of educational data analytics.

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Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Dutta¹,

Alqahtani²,

Albagory³

et al. 2023

Computer Systems Science and Engineering

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Learning Management System (LMS) is an application software that is used in automation, delivery, administration, tracking, and reporting of courses and programs in educational sector. The LMS which exploits machine learning (ML) has the ability of accessing user data and exploit it for improving the learning experience. The recently developed artificial intelligence (AI) and ML models helps to accomplish effective performance monitoring for LMS. Among the different processes involved in ML based LMS, feature selection and classification processes find beneficial. In this motivation, this study introduces Glowwormbased Feature Selection with Machine Learning Enabled Performance Monitoring (GSO-MFWELM) technique for LMS. The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS. The proposed GSO-MFWELM technique involves GSO-based feature selection technique to select the optimal features. Besides, Weighted Extreme Learning Machine (WELM) model is applied for classification process whereas the parameters involved in WELM model are optimally fine-tuned with the help of Mayfly Optimization (MFO) algorithm. The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance. The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects. The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

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An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery

Cited by 5 publications

References 18 publications

Analysis and Prediction of Students’ Academic Performance Based on Educational Data Mining

Analysis and Prediction of Students’ Academic Performance Based on Educational Data Mining

An Adaptive Feature Selection Algorithm for Student Performance Prediction

Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

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