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- W4322484299 abstract "The main objective of this research paper is to overcome the issue of imbalances in data and achieve an improved outcome for grouping by using synthetic minority over-sampling (SMOTE). The imbalanced issue with the dataset causes classification efficiency loss in many applications of data mining comprising of pattern detection, document categorization, and knowledge filtering activities. A sampling-based algorithm SMOTE is used herein to boost the efficiency of emotion classification that over-samples minority class instances to amount of majority class instances. YouTube© dataset was optimized using the SMOTE methodology and evaluated by the use of 3 machine learning algorithms, including decision tree (DT), multinomial Naïve Bayes (MNB), and support vector machines (SVM). As a consequence, SVM reaches full specificity with 92.20 percent filtering function and 88.33 percent filtering process classification. The oversampling solution, however, might not be successful because it merely replicates minority class instances without any additional details. A way to create a set of instances comparable to those in the minority class with high potential to identify with the minority class was proposed in order to address this issue, and use them instead in the oversampling phase." @default.
- W4322484299 created "2023-02-28" @default.
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- W4322484299 date "2023-01-01" @default.
- W4322484299 modified "2023-09-25" @default.
- W4322484299 title "Enhancing the emotion task classification in the imbalanced dataset of YouTube© using synthetic minority over-sampling technique" @default.
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- W4322484299 doi "https://doi.org/10.1063/5.0125068" @default.
- W4322484299 hasPublicationYear "2023" @default.
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