Journal articles on the topic 'Synthetic minority over sampling technique'
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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.
Full textBunkhumpornpat, 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.
Full textShoohi, 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.
Full textAnusha, 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.
Full textBunkhumpornpat, 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.
Full textTarawneh, 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.
Full textDuan, 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.
Full textRaveendhran, 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.
Full textChakrabarty, 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.
Full textSinggalen, 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.
Full textPurnawan, 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.
Full textBelluano, 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.
Full textYulia, 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.
Full textWang, 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.
Full textPoetri, 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.
Full textWibisono, 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.
Full textAntonio, 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.
Full textSulistiyono, 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.
Full textSoltanzadeh, 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.
Full textAmirullah, 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.
Full textIntayoad, Wacharawan, Chayapol Kamyod, and Punnarumol Temdee. "Synthetic Minority Over-Sampling for Improving Imbalanced Data in Educational Web Usage Mining." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 12, no. 2 (2019): 118–29. http://dx.doi.org/10.37936/ecti-cit.2018122.133280.
Full textMalhotra, 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.
Full textAl-Khazaleh, Maisa J., Marwah Alian, and Manar A. Jaradat. "Sentiment analysis of imbalanced Arabic data using sampling techniques and classification algorithms." Bulletin of Electrical Engineering and Informatics 13, no. 1 (2024): 607–18. http://dx.doi.org/10.11591/eei.v13i1.5886.
Full textJung, Ilok, Jaewon Ji, and Changseob Cho. "EmSM: Ensemble Mixed Sampling Method for Classifying Imbalanced Intrusion Detection Data." Electronics 11, no. 9 (2022): 1346. http://dx.doi.org/10.3390/electronics11091346.
Full textZazzaro, Gaetano. "COSM: Controlled Over-Sampling Method." Transactions on Machine Learning and Artificial Intelligence 8, no. 2 (2020): 42–51. http://dx.doi.org/10.14738/tmlai.82.7925.
Full textSandeep, Yadav. "A Comparative Analysis of Sampling Techniques for Imbalanced Datasets in Machine Learning." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 7, no. 5 (2021): 1–7. https://doi.org/10.5281/zenodo.14203644.
Full textKasemtaweechok, 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.
Full textKasemtaweechok, 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.
Full textNeelam, Rout, Mishra Debahuti, and Kumar Mallick Manas. "An advance extended binomial GLMBoost ensemble method with synthetic minority over-sampling technique for handling imbalanced datasets." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 4357–68. https://doi.org/10.11591/ijece.v13i4.pp4357-4368.
Full textBhuiyan, Rabiul Alam, Mst. Shimu Khatun, Md Taslim, and Md.Alam Hossain. "Handling Class Imbalance in Credit Card Fraud Using Various Sampling Techniques." American Journal of Multidisciplinary Research and Innovation 1, no. 4 (2022): 160–68. https://doi.org/10.54536/ajmri.v1i4.633.
Full textKrishnan, Ulagapriya, and Pushpa Sangar. "A Rebalancing Framework for Classification of Imbalanced Medical Appointment No-show Data." Journal of Data and Information Science 6, no. 1 (2021): 178–92. http://dx.doi.org/10.2478/jdis-2021-0011.
Full textChohan, 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.
Full textKarthik, 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.
Full textPrasojo, 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.
Full textJulian, 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.
Full textA, Krishnapriya, and al. et. "Machine Learning For Medicare Fraud Detection: Tackling Class Imbalance With SMOTE-ENN." International Journal of Computational Learning & Intelligence 4, no. 4 (2025): 716–24. https://doi.org/10.5281/zenodo.15251088.
Full textRout, Neelam, Debahuti Mishra, and Manas Kumar Mallick. "An advance extended binomial GLMBoost ensemble method with synthetic minority over-sampling technique for handling imbalanced datasets." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 4357. http://dx.doi.org/10.11591/ijece.v13i4.pp4357-4368.
Full textS, 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.
Full textMANGATAYARU, PURETI, and NARESH. "SPAM MESSAGE DETECTION OVER SOCIAL MEDIA: A SUPERVISED SAMPLING APPROACH FOR THE SOCIAL WEB OF THINGS." Journal of Engineering Sciences 16, no. 04 (2025): 140–46. https://doi.org/10.36893/jes.2025.v16i04.023.
Full textLiu, Zhen-Tao, Bao-Han Wu, Dan-Yun Li, Peng Xiao, and Jun-Wei Mao. "Speech Emotion Recognition Based on Selective Interpolation Synthetic Minority Over-Sampling Technique in Small Sample Environment." Sensors 20, no. 8 (2020): 2297. http://dx.doi.org/10.3390/s20082297.
Full textZhang, Yining. "Machine learning with oversampling for space debris classification based on radar cross section." Applied and Computational Engineering 49, no. 1 (2024): 102–8. http://dx.doi.org/10.54254/2755-2721/49/20241070.
Full textDharmendra, I. Komang, I. Made Agus Wirahadi Putra, and Yohanes Priyo Atmojo. "Evaluation of the Effectiveness of SMOTE and Random Under Sampling in Emotion Classification of Tweets." INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics 9, no. 2 (2024): 182. https://doi.org/10.51211/itbi.v9i2.3183.
Full textHadjadj, Hassina, and Halim Sayoud. "Arabic Authorship Attribution Using Synthetic Minority Over-Sampling Technique and Principal Components Analysis for Imbalanced Documents." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (2021): 1–17. http://dx.doi.org/10.4018/ijcini.20211001.oa33.
Full textAlmonzer, Salah Nooraldaim, Elobaid Ahmed Abdalla Amal, and Mirghani Seed Amna. "An Advanced Machine Learning Approach for Enhanced Diabetes Prediction." International Journal of Current Science Research and Review 07, no. 12 (2024): 8779–89. https://doi.org/10.5281/zenodo.14292064.
Full textBui, My Thi Thien. "Incremental Ensemble Learning Model for Imbalanced Data: a Case Study of Credit Scoring." Journal of Advanced Engineering and Computation 7, no. 2 (2023): 105. http://dx.doi.org/10.55579/jaec.202372.407.
Full textNedjar, Imane, Mohamed Amine Chikh, and Saïd Mahmoudi. "A topological approach for mammographic density classification using a modified synthetic minority over-sampling technique algorithm." International Journal of Biomedical Engineering and Technology 38, no. 2 (2022): 193. http://dx.doi.org/10.1504/ijbet.2022.10045038.
Full textNedjar, Imane, Saïd Mahmoudi, and Mohamed Amine Chikh. "A topological approach for mammographic density classification using a modified synthetic minority over-sampling technique algorithm." International Journal of Biomedical Engineering and Technology 38, no. 2 (2022): 193. http://dx.doi.org/10.1504/ijbet.2022.120870.
Full textFeng, Wei, Gabriel Dauphin, Wenjiang Huang, et al. "Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest for the Classification of Imbalanced Hyperspectral Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 7 (2019): 2159–69. http://dx.doi.org/10.1109/jstars.2019.2922297.
Full textZhang, Xiaolong, Xiaoli Lin, Jiafu Zhao, Qianqian Huang, and Xin Xu. "Efficiently Predicting Hot Spots in PPIs by Combining Random Forest and Synthetic Minority Over-Sampling Technique." IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, no. 3 (2019): 774–81. http://dx.doi.org/10.1109/tcbb.2018.2871674.
Full textHao, Ming, Yanli Wang, and Stephen H. Bryant. "An efficient algorithm coupled with synthetic minority over-sampling technique to classify imbalanced PubChem BioAssay data." Analytica Chimica Acta 806 (January 2014): 117–27. http://dx.doi.org/10.1016/j.aca.2013.10.050.
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