Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 30 for “"SMOTE"”.
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Supervised Classification of Imbalanced Bidding Fraud Data
… and compare several advanced over-sampling (SMOTE), under-sampling (NearMiss and ClusterCentroid) and hybrid sampling (SMOTE-ENN and SMOTE-TomekLink) methods to solve the imbalanced learning problem. We utilize the Randomized Search Cross Validation to tune the hyper-parameters for Support …
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Improved adaptive semi-unsupervised weighted oversampling (IA-SUWO) using sparsity factor for imbalanced datasets
… with existing oversampling techniques such as SMOTE, Borderline-SMOTE, Safe-level SMOTE, and standard A-SUWO technique in terms of accuracy. As aforementioned, the comparative analysis revealed that the proposed oversampling approach performance increased in average by 5% from 85% to 90% than …
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Iceberg Stability Investigations Using Machine Learning for Alaska and Greenland
… methods: no sampling (85.07%), RMU (65.10%), SMOTE (66.14%). For Greenland, we speculate the data set may be too small and generalizes most icebergs as stable and the logistic regression seemingly has more difficulty with classification: no sampling (79.16%), RMU (57.14%), SMOTE (80.10%).
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Modelling highly imbalanced credit card fraud detection data using statistical learning
… Synthetic Minority Oversampling Technique (SMOTE), and Random Undersampling (RUS). These methods are used to create varying levels of imbalance in the datatset, at the prevalence rates of 0.2%, 1%, 10%, 20%, 30%, 40%, and 50%. Six supervised learning models are then used to identify …
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Improveing F-beta Score in Classifying Shark Data into Shark Behaviors
… Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are utilized to balance the data, from which pre-processed Fast Fourier Transform (FFT), Walsh-Hadamard Transform (WHT), and Autocorrelation (AC) features are extracted then classified using …
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The Effect of Hypertension on Periapical Disease: a Retrospective Study Utilizing Machine Learning to Assess a Dental School Population
… so synthetic minority oversampling technique (SMOTE) was used for statistical analysis. After the application of SMOTE, XGB, random forest, Lasso, and SVM algorithms built separate models designed within R to predict periapical disease from the patients’ relevant information. Results: The …
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Fitting AdaBoost Models From Imbalanced Data with Applications in College Basketball
… Synthetic Minority Oversampling Technique’s (SMOTE) and Jittering with Over/Undersampling (JOUS) performed the best, with the JOUS approach being the most accurate for all levels of data imbalance in the simulation study. We then applied the most effective over/undersampling methods to predict …
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DATA MINING AND IMAGE CLASSIFICATION USING GENETIC PROGRAMMING
… imbalanced data classification using GP and SMOTE which was tested on satellite images. The finding showed that the proposed approach improves both training and test results when the SMOTE technique is incorporated. We compared our approach in terms of speed with previous GP algorithms as …
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Cape Town road traffic accident analysis: Utilising supervised learning techniques and discussing their effectiveness
… the synthetic minority oversampling technique (SMOTE). The RTA data was split into training, validation and test sets keeping the proportions of the injury-severity category consistent. Four training datasets were analysed: the original imbalanced data, data with the minority class over-sampled, …
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See Something, Say Nothing? How an Auditor's Moral Judgments Influence Detected Fraud Reporting
… to report detected immaterial fraud. I used the SMOTE technique to generate synthetic observations, supplementing my participant sample. While the results of this study did not support my hypotheses, my examination illustrates auditors overwhelmingly report fraud (95%), regardless of its …
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Machine learning approaches to improving mispronunciation detection on an imbalanced corpus
… and synthetic minority over-sampling technique (SMOTE) were applied to a range of classifiers using feature sets that included information about the acoustic signal, the linguistic properties of the utterance, and word identity. Empirical experiments demonstrate that both balancing approaches …
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Optimising credit card fraud detection through machine learning and deep learning with spatial-temporal imbalance handling
… synthetic minority over-sampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and random under sampling. We assessed eight machine learning algorithms—Bagging Classifier, Random Forest, CatBoost, Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), AdaBoost, Gaussian …
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Performance evaluation of text augmentation methods with BERT on imbalanced datasets
… methods for imbalanced data, including boosting, SMOTE, and simple oversampling, combined with widely used machine learning models, including logistic regression, fully connected neural network, and LSTM. Experimental results show that Word2Vec augmentation improves the performance of BERT in …
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Electrocardiogram-based detection of heart pathologies using nonlinear methods and machine learning
… διασταυρούμενη επικύρωση, επαναδειγματοληψία SMOTE και βελτιστοποίηση κατωφλίου απόφασης βάσει του δείκτη Youden (J). Τα μοντέλα με την καλύτερη απόδοση παρουσίασαν ισχυρή διακριτική ικανότητα (AUC ≈ 0,856–0,919) και ανέδειξαν σχέσεις χαρακτηριστικών–παθολογίας που ευθυγραμμίζονται με γνωστές …
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Estudio predictivo de los factores ǫue participan de la deserción estudiantil en carreras de ciencias médicas de una universidad de gestión privada. Perspectiva que brinda la minería de datos educativa para anticipar el abandono
… con la utilización de técnicas de sobremuestreo (SMOTE –K vecinos cercanos), se incluyen las variables categóricas y cuantitativas, y se realizan las predicciones para cada una de las categorías. Luego se comparan los hallazgos de los dos mejores modelos de Regresión Logística (modelos 2b y 4b) y …
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Predictive modeling of postpartum depression risk using electronic health record data
… the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The analytic cohort demonstrated a PPD prevalence of 7.15% (n=514). The Logistic Regression model achieved the highest predictive performance with an AUC of 0.750, outperforming traditional baseline screening tools in …
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Εφαρμογή αλγορίθμων μηχανικής μάθησης για την υποστήριξη θεραπευτικής απόφασης και την πρόβλεψη χρήσης ενδοστοματικού νάρθηκα σε ασθενείς με υπνική άπνοια
… της εξισορρόπησης δεδομένων με την τεχνική SMOTE, στο πολυκατηγορικό και στο δυαδικό ζητούμενο διερεύνησης. Συμπερασματικά, από τα μοντέλα μηχανικής μάθησης που εφαρμόστηκαν ο Random Forest και ο XGBoost έδειξαν περισσότερη σταθερότητα σε σχέση με τον SVM, αλλά κανένα μοντέλο δεν έδειξε …
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Graph neural network approaches and real-time unsupervised learning for anomaly detection in vehicular networks
… the Synthetic Minority Over-sampling Technique (SMOTE) improved recall, particularly for GCN and T-GCN. Performance was further enhanced by edge weight learning, which adaptively emphasized critical inter-vehicle relationships, and supervised triplet loss, which improved class separability. For …
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Machine Learning Approaches for Improving Construction Materials and Pavement Systems
… the Synthetic Minority Over-sampling Technique (SMOTE) to over-sample and CatBoost to build a predictive model. The results indicated that particle size distribution optimization will improve concrete strength and could be useful information in the future design of materials for sustainable …
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