Relevant bibliographies by topics / Synthetic minority over sampling technique-Tomek

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Academic literature on the topic 'Synthetic minority over sampling technique-Tomek'

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Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Synthetic minority over sampling technique-Tomek"

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Raveendhran, Nareshkumar, and Nimala Krishnan. "A novel hybrid SMOTE oversampling approach for balancing class distribution on social media text." Bulletin of Electrical Engineering and Informatics 14, no. 1 (2025): 638–46. http://dx.doi.org/10.11591/eei.v14i1.8380.

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Depression is a frequent and dangerous medical disorder that has an unhealthy effect on how a person feels, thinks, and acts. Depression is also quite prevalent. Early detection and treatment of depression may avoid painful and perhaps life-threatening symptoms. An imbalance in the data creates several challenges. Consequently, the majority learners will have biases against the class that constitutes the majority and, in extreme situations, may completely dismiss the class that constitutes the minority. For decades, class disparity research has employed traditional machine learning methods. In
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Safriandono, Achmad Nuruddin, De Rosal Ignatius Moses Setiadi, Akhmad Dahlan, Farah Zakiyah Rahmanti, Iwan Setiawan Wibisono, and Arnold Adimabua Ojugo. "Analyzing Quantum Feature Engineering and Balancing Strategies Effect on Liver Disease Classification." Journal of Future Artificial Intelligence and Technologies 1, no. 1 (2024): 51–63. http://dx.doi.org/10.62411/faith.2024-12.

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This research aims to improve the accuracy of liver disease classification using Quantum Feature Engineering (QFE) and the Synthetic Minority Over-sampling Tech-nique and Tomek Links (SMOTE-Tomek) data balancing technique. Four machine learning models were compared in this research, namely eXtreme Gradient Boosting (XGB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) on the Indian Liver Patient Dataset (ILPD) dataset. QFE is applied to capture correlations and complex patterns in the data, while SMOTE-Tomek is used to address data imbalances. The results showed
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Dadang, Dadang, Rahmat Gernowo, and R. Rizal Isnanto. "Over-Under Sampling Approach with Adaptive Synthetic and Tomek Links Methods to Handle Data Imbalance in Sentence Classification on Halal Assurance Certificate Documents." Fusion: Practice and Applications 19, no. 2 (2025): 194–210. https://doi.org/10.54216/fpa.190215.

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Data imbalance is a common problem in machine learning, specifically in classification, in which examples in a dominant class outnumber examples in a minority class many times over. Besides, such a problem keeps a model unable to discover meaningful patterns for a minority class —hence, such a problem reduces model performance specifically in terms of Recall and F1-Score. In current work, activity is performed in overcoming data imbalance problem in sentence classification model of documents of assurance certificate for halal with a combination of over-sampling and under-sampling techniques, n
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Chawla, N. V., K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. "SMOTE: Synthetic Minority Over-sampling Technique." Journal of Artificial Intelligence Research 16 (June 1, 2002): 321–57. http://dx.doi.org/10.1613/jair.953.

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An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the se
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Setiadi, De Rosal Ignatius Moses, Kristiawan Nugroho, Ahmad Rofiqul Muslikh, Syahroni Wahyu Iriananda, and Arnold Adimabua Ojugo. "Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition." Journal of Future Artificial Intelligence and Technologies 1, no. 1 (2024): 23–38. http://dx.doi.org/10.62411/faith.2024-11.

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This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing
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Sinap, Vahid. "Bankruptcy Prediction with Optuna-Enhanced Ensemble Machine Learning Methods: A Comparison of Oversampling and Undersampling Techniques." DÜMF Mühendislik Dergisi 16, no. 1 (2025): 97–113. https://doi.org/10.24012/dumf.1597564.

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Bankruptcy prediction is an essential task in financial risk management, often hindered by challenges such as class imbalance, feature selection, and overfitting. This study investigates the comparative effectiveness of data balancing techniques, specifically focusing on oversampling with SMOTE (Synthetic Minority Over-sampling Technique) and undersampling with Tomek Links, in addressing class imbalance in bankruptcy datasets. A range of machine learning models, including ensemble and boosting algorithms such as Stacking Classifier and XGBoost, were applied to imbalanced, SMOTE-balanced, and T
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Abdullahi, Dauda Sani, Dr Muhammad Sirajo Aliyu, and Usman Musa Abdullahi. "Comparative analysis of resampling algorithms in the prediction of stroke diseases." UMYU Scientifica 2, no. 1 (2023): 88–94. http://dx.doi.org/10.56919/usci.2123.011.

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Stroke disease is a serious cause of death globally. Early predictions of the disease will save a lot of lives but most of the clinical datasets are imbalanced in nature including the stroke dataset, making the predictive algorithms biased towards the majority class. The objective of this research is to compare different data resampling algorithms on the stroke dataset to improve the prediction performances of the machine learning models. This paper considered five (5) resampling algorithms namely; Random over Sampling (ROS), Synthetic Minority oversampling Technique (SMOTE), Adaptive Syntheti
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Tekkali, Chandana Gouri, and Karthika Natarajan. "An advancement in AdaSyn for imbalanced learning: An application to fraud detection in digital transactions." Journal of Intelligent & Fuzzy Systems 46, no. 5-6 (2024): 11381–96. http://dx.doi.org/10.3233/jifs-236392.

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Imbalanced Learning is a significant issue in machine learning, affecting the performance and accuracy of binary or multi-classification algorithms, especially in large-scale data handling and classification. There are some popular techniques to covert this imbalanced data into a balanced one such as undersampling, under-sampling with tomek links, randomized oversampling, synthetic minority oversampling technique (SMOTE), and adaptive synthetic generation (ADASYN). Generally, the ADASYN algorithm could be used to propagate minority sample points to rise the imbalanced ratio between majority an
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Bunkhumpornpat, Chumphol, Krung Sinapiromsaran, and Chidchanok Lursinsap. "DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique." Applied Intelligence 36, no. 3 (2011): 664–84. http://dx.doi.org/10.1007/s10489-011-0287-y.

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Sandeep, Yadav. "SMOTE in Predictive Modeling: A Comprehensive Evaluation of Synthetic Oversampling for Class Imbalance." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 8, no. 4 (2020): 1–9. https://doi.org/10.5281/zenodo.14259555.

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Class imbalance is a pervasive challenge in predictive modeling, where minority class instances are significantly underrepresented, leading to biased models and suboptimal performance. Synthetic Minority Over-sampling Technique (SMOTE) is one of the most widely used solutions to address this issue by generating synthetic samples for the minority class. This study provides a comprehensive evaluation of SMOTE and its variants in handling class imbalance across diverse datasets and model types. We assess SMOTE’s impact on predictive performance, model generalizability, and stability under d
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Dissertations / Theses on the topic "Synthetic minority over sampling technique-Tomek"

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Lin, Yi-Hsien, and 林宜憲. "Constructing a Credit Risk Assessment Model using Synthetic Minority Over-sampling Technique." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/11786273799598686385.

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碩士<br>國立交通大學<br>工業工程與管理學系<br>100<br>The main source of revenue of financial institutions is the interest they charge from their customers. But not all the customers will pay back their debt, financial institutions need to adopt some kind of risk assessment models in order to measure this credit risk. It is not uncommon to observe class imbalance problem in finance risk data. Class imbalance problem is asymmetric categories within data, that is, there is one class of data (major class) significantly outnumbered others (minor class). If we trained a model with imbalanced data, while the accuracy
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Chen, Shih-Cheng, and 陳世承. "An Improved Synthetic Minority Over-sampling Technique for Imbalanced Data Set Learning." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/9g74vs.

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碩士<br>國立清華大學<br>資訊工程學系所<br>105<br>When a few categories of instances of a data set have fewer instances than other categories, such data sets may imply a problem of category imbalances, meaning that the trained classification model is likely to be found for a small number of instances Low cause, and a small number of instances of the wrong case to determine the majority of categories of examples. It is a solution to the distribution of imbalances between the majority of categories and the few categories through the artificial minority category data examples. A variety of algorithms have been d
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鄒景隆. "Novel sampling methods based on synthetic minority over-sampling technique(SMOTE)for imbalanced data classification." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/ek4vzp.

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Limanto, Lisayuri, and 林芳婷. "A Hybrid Inference Model Based on Synthetic Minority Over-sampling Technique and Evolutionary Least Square SVM for Predicting Construction Contractor Default Status." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/46227772514646532070.

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碩士<br>國立臺灣科技大學<br>營建工程系<br>101<br>Construction industry has several typical characteristics that are different compared to other economy sectors, including the dependability among project stakeholders. Thus, financial status of a construction company is an important issue in the construction industry. Assessing the financial status is challenging and the mapping relationship of input factors and the financial status of a company is very complicated. To avoid biased result and represent company’s financial condition, all available construction firm-years data in verified database center is empl
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Tsai, Meng-Fong, and 蔡孟峰. "Application and Study of imbalanced datasets base on Top-N Reverse k-Nearest Neighbor (TRkNN) coupled with Synthetic Minority Over-Sampling Technique (SMOTE)." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/38104987938865711006.

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博士<br>國立中興大學<br>資訊科學與工程學系<br>105<br>The imbalanced classification means the dataset has an unequal class distribution among its population. For a given dataset without considering the imbalanced issue, most classification methods often predict the high accuracy for the majority class, but significantly low accuracy for the minority class. The first task in this dissertation is to provide an efficient algorithm, Top-N Reverse k-Nearest Neighbor (TRkNN), coupled with Synthetic Minority Over-Sampling TEchnique (SMOTE) to overcome this issue for several imbalanced datasets from famous UCI datasets
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Book chapters on the topic "Synthetic minority over sampling technique-Tomek"

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Ramisetty, Uma Maheswari, Venkata Nagesh Kumar Gundavarapu, Akanksha Mishra, and Sravana Kumar Bali. "Analysis of Fraud Detection Prediction Using Synthetic Minority Over-Sampling Technique." In Atlantis Highlights in Intelligent Systems. Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6239-266-3_2.

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Ramisetty, Uma Maheswari, Venkata Nagesh Kumar Gundavarapu, Akanksha Mishra, and Sravana Kumar Bali. "Analysis of Fraud Detection Prediction Using Synthetic Minority Over-Sampling Technique." In Atlantis Highlights in Intelligent Systems. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-074-9_2.

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Khan, Imran, Atta Ur Rahman, and Ahthasham Sajid. "Predictive Modeling for Food Security Assessment Using Synthetic Minority Over-Sampling Technique." In Information Systems Engineering and Management. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81481-5_7.

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Mohd, Fatihah, Masita Abdul Jalil, Noor Maizura Mohamad Noora, Suryani Ismail, Wan Fatin Fatihah Yahya, and Mumtazimah Mohamad. "Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36365-9_8.

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Bunkhumpornpat, Chumphol, Krung Sinapiromsaran, and Chidchanok Lursinsap. "Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_43.

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Adil S. Hasan, Ali S. Saad Azhar, Raza Kamran, and Hussaan A. Mahmood. "An Improved Intrusion Detection Approach using Synthetic Minority Over-Sampling Technique and Deep Belief Network." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-434-3-94.

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This paper presents a network intrusion detection technique based on Synthetic Minority Over-Sampling Technique (SMOTE) and Deep Belief Network (DBN) applied to a class imbalance KDD-99 dataset. SMOTE is used to eliminate the class imbalance problem while intrusion classification is performed using DBN. The proposed technique first resolves the class imbalance problem in the KDD-99 dataset followed by DBN to estimate the initial model. The accuracy is further enhanced by using multilayer perceptron networks. The obtained results are compared with the existing best technique based on reduced si
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Rivera, William, Amit Goel, and J. Peter Kincaid. "Advances in Algorithms for Re-Sampling Class-Imbalanced Educational Data Sets." In Developing Effective Educational Experiences through Learning Analytics. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9983-0.ch002.

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Real world data sets often contain disproportionate sample sizes of observed groups making it difficult for predictive analytics algorithms. One of the many ways to combat inherent bias from class imbalance data is to perform re-sampling. In this book chapter we discuss popular re-sampling methods proposed in research literature, such as Synthetic Minority Over-sampling Technique (SMOTE) and Propensity Score Matching (PSM). We provide an insight into recent advances and our own novel algorithms under the umbrella term of Over-sampling Using Propensity Scores (OUPS). Using simulation we conduct
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Rohith R, Sakthi Jaya Sundar Rajasekar, Thangavel Murugan, and Varalakshmi Perumal. "Enhanced Handwriting Kinematic Modeling for Alzheimer’s Disease Classification Using Machine Learning Models." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250684.

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Alzheimer’s Disease (AD) is a neurodegenerative disorder that gradually deteriorates motor and cognitive abilities, including handwriting abilities. This study explores the effectiveness of handwriting analysis in detecting AD by leveraging Machine Learning (ML) techniques. A dataset containing handwriting samples was preprocessed using normalization and Synthetic Minority Over-Sampling Technique (SMOTE) to balance class distribution. Multiple ML models were trained and evaluated. Among the tested models, the highest classification accuracy, 99.26%, was attained by Multi-Layer Perceptron (MLP)
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Olinsky, Alan, John Thomas Quinn, and Phyllis A. Schumacher. "Visualization of Predictive Modeling for Big Data Using Various Approaches When There Are Rare Events at Differing Levels." In Advances in Data Mining and Database Management. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3142-5.ch021.

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Many techniques exist for predictive modeling of a bivariate target variable in large data sets. When the target variable represents a rare event with an occurrence in the data set of approximately 10% or less, traditional modeling techniques may fail to identify the rare events. In this chapter, different methods, including oversampling of rare events, undersampling of common events and the Synthetic Minority Over-Sampling Technique are used to improve the prediction outcomes of rare events. The predictive models of decision trees, logistic regression and rule induction are applied with SAS E
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Sowmyayani, S. "Predictive Analysis of Diabetes Prediction." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-4252-7.ch004.

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Deep learning algorithms are used in several applications. The development of technology should help humans in saving their life. Diabetes prediction is crucial for medical practitioners. In this chapter, diabetes is predicted using ensemble learning from the medical data. Three classifiers, Naive Bayes (NB), Random Forest (RF) and Neural Network (NN) are selected and defined three estimators for stacking. Grid search is performed to find the best hyperparameters of each estimator. The Stacking Classifier is then used to combine the predictions of the three base estimators and make the final p
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Conference papers on the topic "Synthetic minority over sampling technique-Tomek"

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Munaye, Yirga Yayeh, Atinkut Molla, Yenework Belayneh, and Bizuayehu Simegnew. "Long Short-Term Memory and Synthetic Minority Over Sampling Technique-Based Network Traffic Classification." In 2024 International Conference on Information and Communication Technology for Development for Africa (ICT4DA). IEEE, 2024. https://doi.org/10.1109/ict4da62874.2024.10777078.

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Krisyesika, Joko Lianto Buliali, and Ahmad Saikhu. "Multi-Class Imbalanced Data Classification Using TwinSVM-One versus All and Synthetic Minority Over-sampling Technique." In 2024 4th International Conference on Communication Technology and Information Technology (ICCTIT). IEEE, 2024. https://doi.org/10.1109/icctit64404.2024.10928525.

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Fatima, Noor, Noman Naseer, Zia Mohy-ud-din, and Hedi A. Guesmi. "Leveraging Synthetic Minority Over-Sampling Technique for Class Imbalance in Machine Learning-based Breast Cancer Diagnosis." In 2024 26th International Multitopic Conference (INMIC). IEEE, 2024. https://doi.org/10.1109/inmic64792.2024.11004371.

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Sujon, Khaled Mahmud, Rohayanti Hassan, and Nusrat Jahan. "Synthetic Minority Over-sampling Technique for Student Performance Prediction: A Comparative Analysis of Ensemble and Linear Models." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11022420.

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Pamungkas, Yuri, Ratri Dwi Indriani, and Zain Budi Syulthoni. "Implementation of Synthetic Minority Over-Sampling Technique in the Anaemia Classification Using the LSTM and Bi-LSTM Algorithms." In 2024 11th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2024. https://doi.org/10.1109/eecsi63442.2024.10776106.

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El-Sayed, Asmaa Ahmed, Mahmood Abdel Manem Mahmood, Nagwa Abdel Meguid, and Hesham Ahmed Hefny. "Handling autism imbalanced data using synthetic minority over-sampling technique (SMOTE)." In 2015 Third World Conference on Complex Systems (WCCS). IEEE, 2015. http://dx.doi.org/10.1109/icocs.2015.7483267.

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Deng, Xi, and Hongmin Ren. "Near-Centric Synthetic Minority Over-sampling Technique for Imbalanced Dataset Learning." In 2023 5th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). IEEE, 2023. http://dx.doi.org/10.1109/mlbdbi60823.2023.10482134.

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Jawa, Misha, and Shweta Meena. "Software Effort Estimation Using Synthetic Minority Over-Sampling Technique for Regression (SMOTER)." In 2022 3rd International Conference for Emerging Technology (INCET). IEEE, 2022. http://dx.doi.org/10.1109/incet54531.2022.9824043.

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Ratih, Iis Dewi, Sri Mumpuni Retnaningsih, Islahulhaq Islahulhaq, and Vivi Mentari Dewi. "Synthetic minority over-sampling technique nominal continous logistic regression for imbalanced data." In THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICS AND SCIENCES (THE 3RD ICMSc): A Brighter Future with Tropical Innovation in the Application of Industry 4.0. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0111804.

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Akkaradamrongrat, Suphamongkol, Pornpimon Kachamas, and Sukree Sinthupinyo. "Classification of Advertisement Text on Facebook Using Synthetic Minority Over-Sampling Technique." In ACAI 2018: 2018 International Conference on Algorithms, Computing and Artificial Intelligence. ACM, 2018. http://dx.doi.org/10.1145/3302425.3302471.

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Reports on the topic "Synthetic minority over sampling technique-Tomek"

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p
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