Relevant bibliographies by topics / Model-based oversampling

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Academic literature on the topic 'Model-based oversampling'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Model-based oversampling"

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Lee, Ji-Na, and Ji-Yeoun Lee. "An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection." Applied Sciences 13, no. 6 (2023): 3571. http://dx.doi.org/10.3390/app13063571.

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The Saarbruecken Voice Database (SVD) is a public database used by voice pathology detection systems. However, the distributions of the pathological and normal voice samples show a clear class imbalance. This study aims to develop a system for the classification of pathological and normal voices that uses efficient deep learning models based on various oversampling methods, such as the adaptive synthetic sampling (ADASYN), synthetic minority oversampling technique (SMOTE), and Borderline-SMOTE directly applied to feature parameters. The suggested combinations of oversampled linear predictive c
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Chang, Young-Soo, Hee-Sung Park, and Il-Joon Moon. "Predicting the Cochlear Dead Regions Using a Machine Learning-Based Approach with Oversampling Techniques." Medicina 57, no. 11 (2021): 1192. http://dx.doi.org/10.3390/medicina57111192.

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Background and Objectives: Determining the presence or absence of cochlear dead regions (DRs) is essential in clinical practice. This study proposes a machine learning (ML)-based model that applies oversampling techniques for predicting DRs in patients. Materials and Methods: We used recursive partitioning and regression for classification tree (CT) and logistic regression (LR) as prediction models. To overcome the imbalanced nature of the dataset, oversampling techniques to duplicate examples in the minority class or to synthesize new examples from existing examples in the minority class were
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Vebriyanti, Lo Mei Ly, Shantika Martha, Wirda Andani, and Setyo Wira Rizki. "Analisis Kelayakan Kredit Menggunakan Classification Tree dengan Teknik Random Oversampling." Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi 12, no. 1 (2024): 1–8. http://dx.doi.org/10.37905/euler.v12i1.24182.

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Credit is providing money or bills based on the agreement between a bank and another party. Lending is inseparable from bad credit risk, so credit analysis must be conducted on prospective debtors before approving a proposed loan. This research aims to analyze creditworthiness using a Classification Tree as a classification method with Random Oversampling to overcome imbalanced data. This study uses secondary data on the status of debtors from a bank in West Kalimantan. Research data amounted to 800 data samples consisting of collectability variables as target variables and 10 independent vari
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Lee, Taehwa, and Soojin Lee. "Transformer-based Intrusion Detection Model with Packet Payload Analysis and Oversampling." Journal of Korean Institute of Information Technology 22, no. 10 (2024): 27–34. http://dx.doi.org/10.14801/jkiit.2024.22.10.27.

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Xu, Yanping, Xiaoyu Zhang, Zhenliang Qiu, Xia Zhang, Jian Qiu, and Hua Zhang. "Oversampling Imbalanced Data Based on Convergent WGAN for Network Threat Detection." Security and Communication Networks 2021 (November 5, 2021): 1–14. http://dx.doi.org/10.1155/2021/9206440.

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Class imbalance is a common problem in network threat detection. Oversampling the minority class is regarded as a popular countermeasure by generating enough new minority samples. Generative adversarial network (GAN) is a typical generative model that can generate any number of artificial minority samples, which are close to the real data. However, it is difficult to train GAN, and the Nash equilibrium is almost impossible to achieve. Therefore, in order to improve the training stability of GAN for oversampling to detect the network threat, a convergent WGAN-based oversampling model called con
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Kang, Hangoo, Dongil Kim, and Sungsu Lim. "Machine Learning-Based Anomaly Detection on Seawater Temperature Data with Oversampling." Journal of Marine Science and Engineering 12, no. 5 (2024): 807. http://dx.doi.org/10.3390/jmse12050807.

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This study deals with a method for anomaly detection in seawater temperature data using machine learning methods with oversampling techniques. Data were acquired from 2017 to 2023 using a Conductivity–Temperature–Depth (CTD) system in the Pacific Ocean, Indian Ocean, and Sea of Korea. The seawater temperature data consist of 1414 profiles including 1218 normal and 196 abnormal profiles. This dataset has an imbalance problem in which the amount of abnormal data is insufficient compared to that of normal data. Therefore, we generated abnormal data with oversampling techniques using duplication,
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Xiong, Chuang, Runhan Zhao, Jingtao Xu, et al. "Construct and Validate a Predictive Model for Surgical Site Infection after Posterior Lumbar Interbody Fusion Based on Machine Learning Algorithm." Computational and Mathematical Methods in Medicine 2022 (August 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/2697841.

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Purpose. Surgical site infection is one of the serious complications after lumbar fusion. Early prediction and timely intervention can reduce the harm to patients. The aims of this study were to construct and validate a machine learning model for predicting surgical site infection after posterior lumbar interbody fusion, to screen out the most important risk factors for surgical site infection, and to explore whether synthetic minority oversampling technique could improve the model performance. Method. This study reviewed 584 patients who underwent posterior lumbar interbody fusion for degener
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García-Vicente, Clara, David Chushig-Muzo, Inmaculada Mora-Jiménez, et al. "Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factors." Applied Sciences 13, no. 7 (2023): 4119. http://dx.doi.org/10.3390/app13074119.

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Machine Learning (ML) methods have become important for enhancing the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML models, because it may bias the learning process and the model generalization ability. In this paper, we consider oversampling methods for generating synthetic categorical clinical data aiming to improve the predictive performance in ML models, and the identification of risk factors for cardiovascular diseases (CVDs). We performed a comparative study of several categorical synthetic data generation meth
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A, Sagaya Priya, and Britto Ramesh Kumar S. "Semi-Supervised Intrusion Detection Based on Stacking and Feature-Engineering to Handle Data Imbalance." Indian Journal of Science and Technology 15, no. 46 (2022): 2548–54. https://doi.org/10.17485/IJST/v15i46.1885.

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Abstract <strong>Objectives:</strong>&nbsp;To design an architecture that can effectively handle the imbalance levels and complexities in the network data to provide qualitative predictions.&nbsp;<strong>Methods:</strong>&nbsp;Experiments were performed with KDD CUP 99 dataset, NSL- KDD dataset and UNSW- NB15 dataset. Comparisons were performed with SAVAERDNN model. Oversampling technique is used for data balancing, and the stacking architecture handles the issue of overtraining introduced due to oversampling.<strong>&nbsp;Findings:</strong>&nbsp;The proposed Stacking and Feature engineeringba
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Fieri, Brillian, Joshua La'la, and Derwin Suhartono. "Introversion-Extraversion Prediction using Machine Learning." JOIV : International Journal on Informatics Visualization 7, no. 4 (2023): 2154. http://dx.doi.org/10.62527/joiv.7.4.1019.

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Introversion and extroversion are personality traits that assess the type of interaction between people and others. Introversion and extraversion have their advantages and disadvantages. Knowing their personality, people can utilize these advantages and disadvantages for their benefit. This study compares and evaluates several machine learning models and dataset balancing methods to predict the introversion-extraversion personality based on the survey result conducted by Open-Source Psychometrics Project. The dataset was balanced using three balancing methods, and fifteen questions were chosen
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Book chapters on the topic "Model-based oversampling"

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Batool, Uzma, Mohd Ibrahim Shapiai, Nordinah Ismail, Hilman Fauzi, and Syahrizal Salleh. "Oversampling Based on Data Augmentation in Convolutional Neural Network for Silicon Wafer Defect Classification." In Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques. IOS Press, 2020. http://dx.doi.org/10.3233/faia200547.

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Silicon wafer defect data collected from fabrication facilities is intrinsically imbalanced because of the variable frequencies of defect types. Frequently occurring types will have more influence on the classification predictions if a model gets trained on such skewed data. A fair classifier for such imbalanced data requires a mechanism to deal with type imbalance in order to avoid biased results. This study has proposed a convolutional neural network for wafer map defect classification, employing oversampling as an imbalance addressing technique. To have an equal participation of all classes
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Varssini Segar, Hema, Puteri Natasha Sofia Zulkafli, and Shuhaida Ismail. "Long Short-Term Memory Network Versus Support Vector Machine for Flood Prediction." In Rainfall - Observations and Modelling [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1003858.

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Malaysia is prone to flood disasters, which are considered the most hazardous natural disasters. This study compares the use of Long Short Term Memory (LSTM) networks and Support Vector Machines (SVM) in predicting future flash floods. Additionally, this study examines the effect of using the Synthetic Minority Oversampling Technique (SMOTE) in order to address imbalanced data. In this study, flooding for the year 2021 will be predicted based on the best-performing model. Experimental results indicated that the treatment had a positive impact on the study’s outcome. An analysis of the outcomes
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Şahinbaş, Kevser. "Decision Support Proposal for Imbalanced Clinical Data." In Advances in Healthcare Information Systems and Administration. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7709-7.ch010.

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The difficult diagnosis of acute appendicitis of patients appealing to the hospital with abdominal pain often leads to unnecessary acute appendicitis operations. Accordingly, the aim of this study is to be able to provide the correct diagnosis whether the existing case indeed necessitates operation or not through machine learning algorithms based on classification. To that purpose, SMOTE, random oversampling, and random undersampling methods were proposed to reduce the negative effects of imbalanced data set problem on classification, and it was benefitted from the risk factors in relation to
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De Francesco, Alessio, Luisa Scaccia, Martin Bohem, and Alessandro Cunsolo. "Bayesian Inference as a Tool to Optimize Spectral Acquisition in Scattering Experiments." In Bayesian Inference [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.103850.

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Nowadays, an increasing number of scattering measurements rely on the use of large-scale research facilities, which is usually granted after highly competitive peer-reviewing and typically for short-time lapses. The optimal use of the allocated time requires rigorous estimates on the reliability of the data analysis, as inferred from the limited statistical accuracy of the measurement. Bayesian inference approaches can significantly help this endeavor by providing investigators with much-needed guidance under challenging decisions on experimental time management. We propose here a method based
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Venkataramana, Lokeswari Y., Shomona Gracia Jacob, VenkataVara Prasad D., et al. "Geometric SMOTE-Based Approach to Improve the Prediction of Alzheimer's and Parkinson's Diseases for Highly Class-Imbalanced Data." In Advances in Electronic Government, Digital Divide, and Regional Development. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-7697-0.ch008.

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In many applications where classification is needed, class imbalance poses a serious problem. Class imbalance refers to having very few instances under one or more classes while the other classes contain sufficient amount of data. This makes the results of the classification to be biased towards the classes containing many numbers of samples comparatively. One approach to handle this problem is by generating synthetic instances from the minority classes. Geometric synthetic minority oversampling technique (G-SMOTE) is used to generate artificial samples. G-SMOTE generates synthetic samples in
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Wang, Yong, and Rong Zhu. "Breast Cancer Classification Based on KNNI Imputation of Missing Data." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia231362.

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Some disease datasets have different degrees of missing, which will lead to the problem of low classification accuracy. To improve the effectiveness of breast cancer disease detection and diagnosis, a classification prediction method combining KNNI and XGBoost was proposed and applied to the classification and analysis of breast cancer data. First, the KNNI method is used to impute the missing data in the breast cancer patient dataset; Then, the original dataset is equalized by the SMOTE oversampling method; Finally, XGBoost is used to extract features that are strongly related to breast cance
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Conference papers on the topic "Model-based oversampling"

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Phanbua, Pornthep, Sujitra Arwatchananukul, and Punnarumol Temdee. "Classification Model of Dementia and Heart Failure in Older Adults Using Extra Trees and Oversampling-Based Technique." In 2025 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON). IEEE, 2025. https://doi.org/10.1109/ectidamtncon64748.2025.10962096.

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Gong, Zhichen, and Huanhuan Chen. "Model-Based Oversampling for Imbalanced Sequence Classification." In CIKM'16: ACM Conference on Information and Knowledge Management. ACM, 2016. http://dx.doi.org/10.1145/2983323.2983784.

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Qin, Xuefei, Feng Duan, Shengwen Hou, and Zhiqiang Cai. "Unbalanced Data Classification Model in PHM Field Based on Oversampling Algorithm." In 2022 Global Reliability and Prognostics and Health Management (PHM-Yantai). IEEE, 2022. http://dx.doi.org/10.1109/phm-yantai55411.2022.9942022.

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Chen, Gang, and Xiaomei Guo. "Research on Oversampling Algorithm for Imbalanced Datasets Based On ARIMA Model." In 2021 33rd Chinese Control and Decision Conference (CCDC). IEEE, 2021. http://dx.doi.org/10.1109/ccdc52312.2021.9602084.

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Kiffe, Axel, Stefan Geng, and Thomas Schulte. "Automated generation of a FPGA-based oversampling model of power electronic circuits." In 2012 EPE-ECCE Europe Congress. IEEE, 2012. http://dx.doi.org/10.1109/epepemc.2012.6397371.

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Tsai, Meng-Hsiun, Kai-Cheng Chuan, Jeng-Jer Shieh, Chia-Tsen Tsai, Yong-Zhi Huang, and Tu-Wei Li. "Microarray Data Analysis and Model Construction Based on Oversampling Approach and Decision Tree." In the 2018 2nd High Performance Computing and Cluster Technologies Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3234664.3234674.

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Kang, Qing, and Yong Liu. "A Machine Learning Prediction Model for Rockburst Based on Oversampling Algorithm and Bayesian-XGBoost." In International Symposium for Geotechnical Safety & Risk. Research Publishing Services, 2022. http://dx.doi.org/10.3850/978-981-18-5182-7_00-08-007.xml.

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Kim, Jaekwon, Youngshin Han, and Jongsik Lee. "Data Imbalance Problem solving for SMOTE Based Oversampling: Study on Fault Detection Prediction Model in Semiconductor Manufacturing Process." In Information Technology and Computer Science 2016. Science & Engineering Research Support soCiety, 2016. http://dx.doi.org/10.14257/astl.2016.133.15.

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He, Haoning, Pingshan Liu, and Liya Zhao. "A Financial Risk Early Warning Model for Listed New Energy Vehicle Companies Based on LSTM: Using SMOTE Oversampling and Simulated Annealing Optimization." In CITCE 2024: the 4th International Conference on Computer, Internet of Things and Control Engineering. ACM, 2024. https://doi.org/10.1145/3705677.3705699.

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Qalandari, Roohullah, Ruizhi Zhong, Cyrus Salehi, et al. "Estimation of Rock Permeability Scores Using Machine Learning Methods." In SPE Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210711-ms.

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Abstract Permeability is an important parameter that describes the flow characteristics of rocks (hydrocarbons in the oil and gas reservoirs or groundwater in aquifers). Currently, laboratory experiments using cored samples and well testing are the main methods to determine rock permeability. However, these methods are time-consuming and/or resource-intensive. This paper proposes a novel machine learning approach to predict permeability scores. Field drilling and wireline data are acquired from 80 wells in the Surat Basin, Australia. The permeability scores are based on petrophysical interpret
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