Oversampling Method Based on Gaussian Distribution and K-Means Clustering

Hassan, Masoud Muhammed; Eesa, Adel Sabry; Mohammed, Ahmed Jameel; Arabo, Wahab Kh.

doi:10.32604/cmc.2021.018280

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…Classifiers are vulnerable when trained on imbalanced class labels ( Hassan et al, 2021 ); with imbalanced labels resulting in classification models that provide unreliable and biased predictions. Before proceeding with the experiment, it is important to ensure that each label is well-balanced.…”

Section: Methodsmentioning

confidence: 99%

“…An important consideration during this stage is ensuring that the dataset used to train the model has a balanced representation of the class labels ( Miguéis et al, 2018 ). If the sample used to train the model has a biased or skewed distribution towards the classes, the resultant model might have poor predictive performance, especially towards the minority class ( Hassan et al, 2021 ).…”

Section: Classificationmentioning

confidence: 99%

“…Precision is used to measure the fraction of instances the classification model considers relevant that actually are relevant ( Aman et al, 2019 ; Hassan et al, 2021 ). This metric is used to quantify the correct positive predictions.…”

Section: Classificationmentioning

confidence: 99%

“…The F 1 score or F-value is the harmonic mean of precision and recall ( Khan & Ghosh, 2021 ; Farsi, 2021 ). As it takes into account both FP and FN, it performs well on balanced and imbalanced datasets ( Hassan et al, 2021 ). F 1 score is measured as: …”

Section: Classificationmentioning

confidence: 99%

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Early detection of student degree-level academic performance using educational data mining

Meghji

Mahoto

Asiri

et al. 2023

PeerJ Computer Science

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Higher educational institutes generate massive amounts of student data. This data needs to be explored in depth to better understand various facets of student learning behavior. The educational data mining approach has given provisions to extract useful and non-trivial knowledge from large collections of student data. Using the educational data mining method of classification, this research analyzes data of 291 university students in an attempt to predict student performance at the end of a 4-year degree program. A student segmentation framework has also been proposed to identify students at various levels of academic performance. Coupled with the prediction model, the proposed segmentation framework provides a useful mechanism for devising pedagogical policies to increase the quality of education by mitigating academic failure and encouraging higher performance. The experimental results indicate the effectiveness of the proposed framework and the applicability of classifying students into multiple performance levels using a small subset of courses being taught in the initial two years of the 4-year degree program.

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Section: Methodsmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

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Early detection of student degree-level academic performance using educational data mining

Meghji

Mahoto

Asiri

et al. 2023

PeerJ Computer Science

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“…[17] GK-Means [17] The F1-score and accuracy have exhibited a general improvement ranging from 1 to 6 percentage points.…”

Section: K-means Smote Ucimentioning

confidence: 99%

A Data Enhancement Algorithm for DDoS Attacks Using IoT

Lv,

Du,

Zhou

et al. 2023

Sensors

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With the rapid development of the Internet of Things (IoT), the frequency of attackers using botnets to control IoT devices in order to perform distributed denial-of-service attacks (DDoS) and other cyber attacks on the internet has significantly increased. In the actual attack process, the small percentage of attack packets in IoT leads to low accuracy of intrusion detection. Based on this problem, the paper proposes an oversampling algorithm, KG-SMOTE, based on Gaussian distribution and K-means clustering, which inserts synthetic samples through Gaussian probability distribution, extends the clustering nodes in minority class samples in the same proportion, increases the density of minority class samples, and improves the amount of minority class sample data in order to provide data support for IoT-based DDoS attack detection. Experiments show that the balanced dataset generated by this method effectively improves the intrusion detection accuracy in each category and effectively solves the data imbalance problem.

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