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

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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Ratantja Kusumajati, Fatwa, Basuki Rahmat, and Achmad Junaidi. "IMPLEMENTATION OF BALANCING DATA METHOD USING SMOTETOMEK IN DIABETES CLASSIFICATION USING XGBOOST." Jurnal Ilmiah Kursor 12, no. 4 (2024): 201–12. https://doi.org/10.21107/kursor.v12i4.410.

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In this research, XGBoost algorithm and the SMOTETomek approach are employed with the objective of enhancing the accuracy of diabetes classification. The study utilises 2,000 patient data points, comprising demographic and medical information, sourced from Kaggle. The dataset employed in this study comprises a number of variables, including pregnancies, glucose levels, blood pressure, skin thickness, insulin levels, Body Mass Index (BMI), diabetes pedigree function, age, and an outcome variable. The latter is a binary classification label, taking on the values 0 and 1. A value of 0 indicates t

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Purnajaya, Akhmad Rezki, and Fuad Dwi Hanggara. "Perbandingan Performa Teknik Sampling Data untuk Klasifikasi Pasien Terinfeksi Covid-19 Menggunakan Rontgen Dada." Journal of Applied Informatics and Computing 5, no. 1 (2021): 37–42. http://dx.doi.org/10.30871/jaic.v5i1.3010.

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Virus COVID-19 menjadi virus yang mematikan dan menggemparkan dunia. Salah satu akibat yang ditimbulkan oleh virus COVID-19 adalah infeksi saluran pernapasan. Solusi yang diajukan untuk masalah ini adalah dengan prediksi infeksi virus COVID-19. Prediksi ini dibuat berdasarkan klasifikasi data rontgen dada. Namun, jumlah data rontgen dada adalah data yang tidak seimbang. Hasil dari ketidakseimbangan data adalah klasifikasi data yang mengabaikan kelas dengan data yang lebih sedikit. Untuk mengatasi masalah tersebut maka teknik pengambilan sampel data menjadi mekanisme untuk membuat data menjadi

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Wu, Liwei, Detao Zhou, Gensheng Li, et al. "Kick Risk Diagnosis Method Based on Ensemble Learning Models." Processes 12, no. 12 (2024): 2704. https://doi.org/10.3390/pr12122704.

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As oil and gas exploration and development gradually advance into deeper and offshore fields, the geological environment and formation pressure conditions become increasingly complex, leading to a higher risk of drilling incidents such as kicks. Timely diagnosis of kick risk is crucial for ensuring safety and efficiency. This study proposes a kick risk diagnosis method based on ensemble learning models, which integrates various time-series analysis algorithms to construct and optimize multiple kick diagnosis models, accurately fitting the relationship between integrated logging parameters and

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Shoohi, Liqaa M., and Jamila H. Saud. "Adaptation Proposed Methods for Handling Imbalanced Datasets based on Over-Sampling Technique." Al-Mustansiriyah Journal of Science 31, no. 2 (2020): 25. http://dx.doi.org/10.23851/mjs.v31i2.740.

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Classification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields. These algorithms speak of the problem of imbalanced data, where there are situations that belong to more classes than others. Imbalanced data result in poor performance and bias to a class without other classes. In this paper, we proposed three techniques based on the Over-Sampling (O.S.) technique for processing imbalanced dataset and redistributing it

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Anusha, Yamijala, R. Visalakshi, and Konda Srinivas. "Imbalanced data classification using improved synthetic minority over-sampling technique." Multiagent and Grid Systems 19, no. 2 (2023): 117–31. http://dx.doi.org/10.3233/mgs-230007.

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In data mining, deep learning and machine learning models face class imbalance problems, which result in a lower detection rate for minority class samples. An improved Synthetic Minority Over-sampling Technique (SMOTE) is introduced for effective imbalanced data classification. After collecting the raw data from PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases, the pre-processing is performed using min-max normalization, cleaning, integration, and data transformation techniques to achieve data with better uniqueness, consistency, completeness and validity. An improved SMOTE algorithm

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Ijaz, Muhammad Fazal, Muhammad Attique, and Youngdoo Son. "Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods." Sensors 20, no. 10 (2020): 2809. http://dx.doi.org/10.3390/s20102809.

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Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for exa

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Bunkhumpornpat, Chumphol, and Krung Sinapiromsaran. "CORE: core-based synthetic minority over-sampling and borderline majority under-sampling technique." International Journal of Data Mining and Bioinformatics 12, no. 1 (2015): 44. http://dx.doi.org/10.1504/ijdmb.2015.068952.

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Tarawneh, Ahmad S., Ahmad B. A. Hassanat, Khalid Almohammadi, Dmitry Chetverikov, and Colin Bellinger. "SMOTEFUNA: Synthetic Minority Over-Sampling Technique Based on Furthest Neighbour Algorithm." IEEE Access 8 (2020): 59069–82. http://dx.doi.org/10.1109/access.2020.2983003.

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Duan, Yijun, Xin Liu, Adam Jatowt, et al. "SORAG: Synthetic Data Over-Sampling Strategy on Multi-Label Graphs." Remote Sensing 14, no. 18 (2022): 4479. http://dx.doi.org/10.3390/rs14184479.

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In many real-world networks of interest in the field of remote sensing (e.g., public transport networks), nodes are associated with multiple labels, and node classes are imbalanced; that is, some classes have significantly fewer samples than others. However, the research problem of imbalanced multi-label graph node classification remains unexplored. This non-trivial task challenges the existing graph neural networks (GNNs) because the majority class can dominate the loss functions of GNNs and result in the overfitting of the majority class features and label correlations. On non-graph data, mi

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Hermawan, Aditiya, Willy Wijaya, and Benny Daniawan. "Optimizing Artificial Neural Network for Customer Churn: Advanced Data Balancing and Feature Selection." JOIV : International Journal on Informatics Visualization 9, no. 3 (2025): 1132. https://doi.org/10.62527/joiv.9.3.3064.

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Customers are valuable assets in the dynamic business world. However, service dissatisfaction often leads them to switch to competitors, a phenomenon known as customer churn. In the telecommunications industry, churn poses a significant challenge as it directly impacts revenue and influences other customers within their social networks to do the same. Consequently, predicting churn has become essential, with numerous researchers employing various methods to classify potential churners. This study builds upon prior research that utilized Artificial Neural Networks (ANN) or Deep Learning to pred

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Mduma, Neema. "Data Balancing Techniques for Predicting Student Dropout Using Machine Learning." Data 8, no. 3 (2023): 49. http://dx.doi.org/10.3390/data8030049.

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Predicting student dropout is a challenging problem in the education sector. This is due to an imbalance in student dropout data, mainly because the number of registered students is always higher than the number of dropout students. Developing a model without taking the data imbalance issue into account may lead to an ungeneralized model. In this study, different data balancing techniques were applied to improve prediction accuracy in the minority class while maintaining a satisfactory overall classification performance. Random Over Sampling, Random Under Sampling, Synthetic Minority Over Samp

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Akinjole, Abisola, Olamilekan Shobayo, Jumoke Popoola, Obinna Okoyeigbo, and Bayode Ogunleye. "Ensemble-Based Machine Learning Algorithm for Loan Default Risk Prediction." Mathematics 12, no. 21 (2024): 3423. http://dx.doi.org/10.3390/math12213423.

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Predicting credit default risk is important to financial institutions, as accurately predicting the likelihood of a borrower defaulting on their loans will help to reduce financial losses, thereby maintaining profitability and stability. Although machine learning models have been used in assessing large applications with complex attributes for these predictions, there is still a need to identify the most effective techniques for the model development process, including the technique to address the issue of data imbalance. In this research, we conducted a comparative analysis of random forest,

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Duarte, Jaqueline Damacena, Guilherme Dantas Bispo, Gabriel Arquelau Pimenta Rodrigues, André Luiz Marques Serrano, Gabriela Mayumi Saiki, and Vinícius Pereira Gonçalves. "Synthetic Minority Over-sampling Technique for detecting Malicious Traffic targeting Internet of Things' devices." Journal of Internet Services and Applications 16, no. 1 (2025): 332–44. https://doi.org/10.5753/jisa.2025.5251.

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This study proposes a multiclass machine learning approach for detecting 34 distinct types of cyberattacks in Internet of Things (IoT) traffic using the CICIoT2023 dataset. We evaluate the performance of lightweight classifiers—Bernoulli Naive Bayes, Decision Tree, Random Forest, and XGBoost—under highly imbalanced conditions. To address class imbalance and improve minority-class detection, we apply the Synthetic Minority Over-sampling Technique (SMOTE). In addition, we conduct hyperparameter tuning using RandomizedSearchCV and assess model performance using macro-average metrics, including re

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Singgalen, Yerik Afrianto. "Performance evaluation of SVM with synthetic minority over-sampling technique in sentiment classification." Jurnal Mantik 8, no. 1 (2024): 326–36. http://dx.doi.org/10.35335/mantik.v8i1.5077.

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This study investigates the performance of the Support Vector Machine (SVM) algorithm in sentiment analysis tasks within the context of tourism destination branding, utilizing the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. Specifically, the research compares SVM performance with and without the Synthetic Minority Over-sampling Technique (SMOTE) across various metrics including accuracy, precision, recall, F-measure, and Area Under the Curve (AUC). The analysis is conducted on a dataset comprising textual data extracted from "Wonderful Indonesia" promotional videos fe

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Gul, Hira, Nadeem Javaid, Ibrar Ullah, Ali Mustafa Qamar, Muhammad Khalil Afzal, and Gyanendra Prasad Joshi. "Detection of Non-Technical Losses Using SOSTLink and Bidirectional Gated Recurrent Unit to Secure Smart Meters." Applied Sciences 10, no. 9 (2020): 3151. http://dx.doi.org/10.3390/app10093151.

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Energy consumption is increasing exponentially with the increase in electronic gadgets. Losses occur during generation, transmission, and distribution. The energy demand leads to increase in electricity theft (ET) in distribution side. Data analysis is the process of assessing the data using different analytical and statistical tools to extract useful information. Fluctuation in energy consumption patterns indicates electricity theft. Utilities bear losses of millions of dollar every year. Hardware-based solutions are considered to be the best; however, the deployment cost of these solutions i

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Belluano, Poetri Lestari Lokapitasari, Reyna Aprilia Rahma, Herdianti Darwis, and Abdul Rachman Manga. "Analysis of ensemble machine learning classification comparison on the skin cancer MNIST dataset." Computer Science and Information Technologies 5, no. 3 (2024): 235–42. http://dx.doi.org/10.11591/csit.v5i3.p235-242.

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This study aims to analyze the performance of various ensemble machine learning methods, such as Adaboost, Bagging, and Stacking, in the context of skin cancer classification using the skin cancer MNIST dataset. We also evaluate the impact of handling dataset imbalance on the classification model’s performance by applying imbalanced data methods such as random under sampling (RUS), random over sampling (ROS), synthetic minority over-sampling technique (SMOTE), and synthetic minority over-sampling technique with edited nearest neighbor (SMOTEENN). The research findings indicate that Adaboost is

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Priya, Shalini, Alina Peluso, Mayanka Chandra Shekhar, Ioana Danciu, Jordan Miller, and Heidi A. Hanson. "Abstract 4191: Assessing performance of biomarker extraction from electronic health records: Data augmentation methods for a hierarchical self-attention network (HiSAN)." Cancer Research 83, no. 7_Supplement (2023): 4191. http://dx.doi.org/10.1158/1538-7445.am2023-4191.

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Abstract Background: Extraction of HER2 status from electronic health records (EHR) may expedite clinical trials matching and be used for survivorship research. Deep learning (DL) algorithms have potential to extract this data; however, inherent class imbalance leads to reduced model performance. We compare state of the art strategies to handle class imbalance in models trained to extract HER2 status. This comparative analysis may be used as a guideline for HER2 extraction. Methods: 680,117 pathology reports collected from 2017-2021 by the National Cancer Institutes’ Surveillance, Epidemiology

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Yulia, Ery Kurniawati, and Denny Prabowo Yulius. "Model optimisation of class imbalanced learning using ensemble classifier on over-sampling data." International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 276–83. https://doi.org/10.11591/ijai.v11.i1.pp276-283.

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Data imbalance is one of the problems in the application of machine learning and data mining. Often this data imbalance occurs in the most essential and needed case entities. Two approaches to overcome this problem are the data level approach and the algorithm approach. This study aims to get the best model using the pap smear dataset that combined data levels with an algorithmic approach to solve data imbalanced. The laboratory data mostly have few data and imbalance. Almost in every case, the minor entities are the most important and needed. Over-sampling as a data level approach used in thi

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Wang, Sheng, Liling Ma, and Junzheng Wang. "Fault Diagnosis Method Based on CND-SMOTE and BA-SVM Algorithm." Journal of Physics: Conference Series 2493, no. 1 (2023): 012008. http://dx.doi.org/10.1088/1742-6596/2493/1/012008.

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Abstract The problem of unbalanced data classification has gotten extensive attention in the past few years. Unbalanced sample data makes the fault diagnosis and classification accuracy rate low, and the capability to classify minority-class fault samples is restricted. To address the problem that the classification algorithm in machine learning has the insufficient capability to identify minority class samples for unbalanced sample data classification problems. Therefore, this paper proposes an improved support vector machine (SVM) classification method based on the synthetic minority over-sa

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Poetri, Lestari Lokapitasari Belluano, Aprilia Rahma Reyna, Darwis Herdianti, and Rachman Manga Abdul. "Analysis of ensemble machine learning classification comparison on the skin cancer MNIST dataset." Computer Science and Information Technologies 5, no. 3 (2024): 235–42. https://doi.org/10.11591/csit.v5i3.pp235-242.

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This study aims to analyze the performance of various ensemble machine learning methods, such as Adaboost, Bagging, and Stacking, in the context of skin cancer classification using the skin cancer MNIST dataset. We also evaluate the impact of handling dataset imbalance on the classification model’s performance by applying imbalanced data methods such as random under sampling (RUS), random over sampling (ROS), synthetic minority over-sampling technique (SMOTE), and synthetic minority over-sampling technique with edited nearest neighbor (SMOTEENN). The research findings indicate that Adabo

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Wibisono, David Leandro, and Zaenal Abidin. "Prediction of Student Graduation Predicts using Hybrid 2D Convolutional Neural Network and Synthetic Minority Over-Sampling Technique." Recursive Journal of Informatics 1, no. 1 (2023): 27–34. http://dx.doi.org/10.15294/rji.v1i1.65646.

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Abstract. With the rapid growth of technology, educational institutions are constantly looking for ways to improve their services and enhance student performance. One of the significant challenges in higher education is predicting the graduation outcome of students. Predicting student graduation can help educators and academic advisors to provide early intervention and support to students who may be at risk of not graduating on time. In this paper, we propose a hybrid 2D convolutional neural network (CNN) and synthetic minority over-sampling technique (SMOTE) to predict the graduation outcome

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Antonio, Roy, and Hironimus Leong. "PERFORMANCE OF SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE ON SUPPORT VECTOR MACHINE AND K-NEAREST NEIGHBOR FOR SENTIMENT ANALYSIS OF METAVERSE IN INDONESIA." Proxies : Jurnal Informatika 6, no. 2 (2024): 160–70. http://dx.doi.org/10.24167/proxies.v6i2.12459.

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The metaverse is one of the most discussed things on social media, Twitter in Indonesia. This view can be both positive and negative in Indonesian society, hence the need for sentiment analysis. However, creating a sentiment classification model with unbalanced data will reduce performance. For this reason, Synthetic Minority Oversampling is needed in Support Vector Machine and K-Nearest Neighbor algorithms. The results of Synthetic Minority Oversampling can improve the accuracy of the Support Vector Machine and K-Nearest Neighbor algorithms.

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Sulistiyono, Mulia, Yoga Pristyanto, Sumarni Adi, and Gagah Gumelar. "Implementasi Algoritma Synthetic Minority Over-Sampling Technique untuk Menangani Ketidakseimbangan Kelas pada Dataset Klasifikasi." SISTEMASI 10, no. 2 (2021): 445. http://dx.doi.org/10.32520/stmsi.v10i2.1303.

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Soltanzadeh, Paria, and Mahdi Hashemzadeh. "RCSMOTE: Range-Controlled synthetic minority over-sampling technique for handling the class imbalance problem." Information Sciences 542 (January 2021): 92–111. http://dx.doi.org/10.1016/j.ins.2020.07.014.

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Amirullah, Afif, Umi Laili Yuhana, and Muhammad Alfian. "Improve Software Defect Prediction using Particle Swarm Optimization and Synthetic Minority Over-sampling Technique." Scientific Journal of Informatics 11, no. 4 (2025): 1127–36. https://doi.org/10.15294/sji.v11i4.16808.

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Purpose: Early detection of software defects is essential to prevent problems with software maintenance. Although much machine learning research has been used to predict software defects, most have not paid attention to the problems of data imbalance and feature correlation. This research focuses on overcoming the problems of imbalance dataset. It provides new insights into the impact of these two feature extraction techniques in improving the accuracy of software defect prediction. Methods: This research compares three algorithms: Random Forest, Logistic Regression, and XGBoost, with the appl

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Setiadi, De Rosal Ignatius Moses, Hussain Md Mehedul Islam, Gustina Alfa Trisnapradika, and Wise Herowati. "Analyzing Preprocessing Impact on Machine Learning Classifiers for Cryotherapy and Immunotherapy Dataset." Journal of Future Artificial Intelligence and Technologies 1, no. 1 (2024): 39–50. http://dx.doi.org/10.62411/faith.2024-2.

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In the clinical treatment of skin diseases and cancer, cryotherapy and immunotherapy offer effective and minimally invasive alternatives. However, the complexity of patient response demands more sophisticated analytical strategies for accurate outcome prediction. This research focuses on analyzing the effect of preprocessing in various machine learning models on the prediction performance of cryotherapy and immunotherapy. The preprocessing techniques analyzed are advanced feature engineering and Synthetic Minority Over-sampling Technique (SMOTE) and Tomek links as resampling techniques and the

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Hassan, Syeda Nida, Mudassir Khalil, Humayun Salahuddin, Rizwan Ali Naqvi, Daesik Jeong, and Seung-Won Lee. "KOC_Net: Impact of the Synthetic Minority Over-Sampling Technique with Deep Learning Models for Classification of Knee Osteoarthritis Using Kellgren–Lawrence X-Ray Grade." Mathematics 12, no. 22 (2024): 3534. http://dx.doi.org/10.3390/math12223534.

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One of the most common diseases afflicting humans is knee osteoarthritis (KOA). KOA occurs when the knee joint cartilage breaks down, and knee bones start rubbing together. The diagnosis of KOA is a lengthy process, and missed diagnosis can have serious consequences. Therefore, the diagnosis of KOA at an initial stage is crucial which prevents the patients from Severe complications. KOA identification using deep learning (DL) algorithms has gained popularity during the past few years. By applying knee X-ray images and the Kellgren–Lawrence (KL) grading system, the objective of this study was t

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Chakrabarty, Navoneel, and Sanket Biswas. "Navo Minority Over-sampling Technique (NMOTe): A Consistent Performance Booster on Imbalanced Datasets." June 2020 2, no. 2 (2020): 96–136. http://dx.doi.org/10.36548/jei.2020.2.004.

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Imbalanced data refers to a problem in machine learning where there exists unequal distribution of instances for each classes. Performing a classification task on such data can often turn bias in favour of the majority class. The bias gets multiplied in cases of high dimensional data. To settle this problem, there exists many real-world data mining techniques like over-sampling and under-sampling, which can reduce the Data Imbalance. Synthetic Minority Oversampling Technique (SMOTe) provided one such state-of-the-art and popular solution to tackle class imbalancing, even on high-dimensional da

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Kasemtaweechok, Chatchai, and Worasait Suwannik. "Under-sampling technique for imbalanced data using minimum sum of euclidean distance in principal component subset." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 305. http://dx.doi.org/10.11591/ijai.v13.i1.pp305-318.

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<span lang="EN-US">Imbalanced datasets are characterized by a substantially smaller number of data points in the minority class compared to the majority class. This imbalance often leads to poor predictive performance of classification models when applied in real-world scenarios. There are three main approaches to handle imbalanced data: over-sampling, under-sampling, and hybrid approach. The over-sampling methods duplicate or synthesize data in the minority class. On the other hand, the under-sampling methods remove majority class data. Hybrid methods combine the noise-removing benefits

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Kasemtaweechok, Chatchai, and Worasait Suwannik. "Under-sampling technique for imbalanced data using minimum sum of euclidean distance in principal component subset." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 305–18. https://doi.org/10.11591/ijai.v13.i1.pp305-318.

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Imbalanced datasets are characterized by a substantially smaller number of data points in the minority class compared to the majority class. This imbalance often leads to poor predictive performance of classification models when applied in real-world scenarios. There are three main approaches to handle imbalanced data: over-sampling, under-sampling, and hybrid approach. The over-sampling methods duplicate or synthesize data in the minority class. On the other hand, the under-sampling methods remove majority class data. Hybrid methods combine the noise-removing benefits of under-sampling the ma

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Purnawan, I. Ketut Adi, Adhi Dharma Wibawa, Arik Kurniawati, and Mauridhi Hery Purnomo. "Optimizing Diabetic Neuropathy Severity Classification Using Electromyography Signals Through Synthetic Oversampling Techniques." Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) 13, no. 3 (2024): 681–90. https://doi.org/10.23887/janapati.v13i3.85675.

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Electromyography signals are electrical signals generated by muscle activity and are very useful for analyzing the health conditions of muscles and nerves. Data imbalance is a prevalent issue in EMG signal data, especially when addressing patients with varied health conditions and restricted data availability. A major difficulty for machine learning models is class imbalance in datasets, which frequently leads to biased predictions favoring the dominant class and neglecting the minority classes. The data augmentation method employs the Synthetic Minority Over Sampling Technique (SMOTE) and Ran

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Chohan, Saifurrachman, Arifin Nugroho, Achmad Maezar Bayu Aji, and Windu Gata. "Analisis Sentimen Pengguna Aplikasi Duolingo Menggunakan Metode Naïve Bayes dan Synthetic Minority Over Sampling Technique." Paradigma - Jurnal Komputer dan Informatika 22, no. 2 (2020): 139–44. http://dx.doi.org/10.31294/p.v22i2.8251.

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Karthik, M., and M. Krishnan. "Detecting Internet of Things Attacks Using Post Pruning Decision Tree-Synthetic Minority Over Sampling Technique." International Journal of Intelligent Engineering and Systems 14, no. 4 (2021): 105–14. http://dx.doi.org/10.22266/ijies2021.0831.10.

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Prasojo, Rahman Azis, Muhammad Akmal A. Putra, Ekojono, et al. "Precise transformer fault diagnosis via random forest model enhanced by synthetic minority over-sampling technique." Electric Power Systems Research 220 (July 2023): 109361. http://dx.doi.org/10.1016/j.epsr.2023.109361.

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Julian, Fajar Azhari, and Fahmi Arif. "Enhancing Cascade Quality Prediction Method in Handling Imbalanced Dataset Using Synthetic Minority Over-Sampling Technique." Industrial Engineering & Management Systems 22, no. 4 (2023): 389–98. http://dx.doi.org/10.7232/iems.2023.22.4.389.

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Bi, Mengzhou, Zhen Guan, Tengjiao Fan, et al. "Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models." Molecules 27, no. 6 (2022): 1753. http://dx.doi.org/10.3390/molecules27061753.

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Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, and considerable progress has been made in the discovery of DYRK1A inhibitors. Identification of pharmacophoric fragments provides valuable information for structure- and fragment-based design of potent and selective DYRK1A inhibitors. In this study, seven machine learning methods along with five molecular fingerprints were employed to develop qualitative classification models of DYRK1A inhibitors, which were evaluated by cross-validation, test s

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Vijayendra S.Gaikwad. "BES-Optimized SMOTE Variant to Improve Dataset Scaling for Enhanced Privacy-Preserving Classification." Panamerican Mathematical Journal 34, no. 4 (2024): 19–40. http://dx.doi.org/10.52783/pmj.v34.i4.1867.

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The research paper undertakes a comprehensive comparative assessment of various Synthetic Minority Over-sampling Technique (SMOTE) variants in the context of medical datasets. The study is dedicated to mitigating the challenges posed by imbalanced data in medical applications. The performance of SMOTE, SMOTE-ENN, Borderline SMOTE, SMOTE-Tomek Links, Safe Level SMOTE, Density-based SMOTE, SMOTE-Rough Set Theory, and a developed BES-Optimized SMOTE are evaluated across five distinct medical datasets such as heart disease, arrhythmia, sick euthyroid, hypothyroid and breast cancer. Additionally, t

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Hu, Guangwu, Maoqi Sun, and Chaoqin Zhang. "A High-Accuracy Advanced Persistent Threat Detection Model: Integrating Convolutional Neural Networks with Kepler-Optimized Bidirectional Gated Recurrent Units." Electronics 14, no. 9 (2025): 1772. https://doi.org/10.3390/electronics14091772.

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Advanced Persistent Threat (APT) refers to a highly targeted, sophisticated, and prolonged form of cyberattack, typically directed at specific organizations or individuals. The primary objective of such attacks is the theft of sensitive information or the disruption of critical operations. APT attacks are characterized by their stealth and complexity, often resulting in significant economic losses. Furthermore, these attacks may lead to intelligence breaches, operational interruptions, and even jeopardize national security and political stability. Given the covert nature and extended durations

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S, Karthikeyan, and Kathirvalavakumar T. "Genetic Algorithm Based Over-Sampling with DNN in Classifying the Imbalanced Data Distribution Problem." Indian Journal of Science and Technology 16, no. 8 (2023): 547–56. https://doi.org/10.17485/IJST/v16i8.863.

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Abstract <strong>Objective:</strong> Data imbalance exists in many real-life applications. In the imbalanced datasets, the minority class data creates a wrong inference during the classification that leads to more misclassification. More research has been done in the past to solve this issue, but as of now there is no global working solution found to do efficient classification. After analyzing various existing literatures, it is proposed to minimize the misclassification through genetic based oversampling and deep neural network (DNN) classifier. <strong>Method:</strong> In the

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Malhotra, Ruchika, and Kishwar Khan. "OpTunedSMOTE: A novel model for automated hyperparameter tuning of SMOTE in software defect prediction." Intelligent Data Analysis: An International Journal 29, no. 3 (2024): 787–807. https://doi.org/10.1177/1088467x241301390.

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Software Defect Prediction plays a crucial role in quality assurance by identifying potential defects early in the software development lifecycle. It is an essential aspect of modern software engineering that significantly contributes to improving software quality and reliability. It utilizes a variety of techniques, including machine learning algorithms like decision trees, support vector machines, and neural networks, to predict defects. A lot of research tried to improve the prediction accuracy but had problems with imbalanced data and hyperparameter tuning of the algorithms. To deal with t

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