Gotowe bibliografie tematyczne / Synthetic minority oversampling technique

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  2. Rozprawy doktorskie
  3. Części książek
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Gotowa bibliografia na temat „Synthetic minority oversampling technique”

Autor: Grafiati
Data publikacji: 2 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Synthetic minority oversampling technique"

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Hooda, Sakshi, and Suman Mann. "Distributed Synthetic Minority Oversampling Technique." International Journal of Computational Intelligence Systems 12, no. 2 (2019): 929. http://dx.doi.org/10.2991/ijcis.d.190719.001.

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Suci, Wulan, and Samsudin Samsudin. "Algoritma K-Nearest Neighbors dan Synthetic Minority Oversampling Technique dalam Prediksi Pemesanan Tiket Pesawat." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 3 (2022): 1775. http://dx.doi.org/10.30865/mib.v6i3.4374.

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This study applies the Synthetic Minority Oversampling Technique to improve the performance of the K-Nearest Neighbors method in predicting the unbalanced data class. Most classification algorithms implicitly assume that the processed data has a balanced distribution, so that the standard classifier is more inclined towards data with a dominant class number (majority class). The use of Synthetic Minority Oversampling Technique can improve the performance of the K-Nearest Neighbors method for flight ticket booking data. Although in terms of accuracy, Synthetic Minority Oversampling Technique wi
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Rahardian, Hanif, Mohammad Reza Faisal, Friska Abadi, Radityo Adi Nugroho, and Rudy Herteno. "IMPLEMENTATION OF DATA LEVEL APPROACH TECHNIQUES TO SOLVE UNBALANCED DATA CASE ON SOFTWARE DEFECT CLASSIFICATION." Journal of Data Science and Software Engineering 1, no. 01 (2020): 53–62. http://dx.doi.org/10.20527/jdsse.v1i01.13.

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Defects can cause significant software rework, delays, and high costs, to prevent disability it must be predictable the possibility of defects. To predict the disability the metrics software dataset is used. NASA MDP is one of the popular software metrics used to predict software defects by having 13 datasets and is generally unbalanced. The reward in the dataset can reduce the prediction of software defects because more unbalanced data produces a majority class. Data imbalance can be handled with 2 approaches, namely the data level approach technique and the algorithm level approach technique
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Gnip, Peter, Liberios Vokorokos, and Peter Drotár. "Selective oversampling approach for strongly imbalanced data." PeerJ Computer Science 7 (June 18, 2021): e604. http://dx.doi.org/10.7717/peerj-cs.604.

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Challenges posed by imbalanced data are encountered in many real-world applications. One of the possible approaches to improve the classifier performance on imbalanced data is oversampling. In this paper, we propose the new selective oversampling approach (SOA) that first isolates the most representative samples from minority classes by using an outlier detection technique and then utilizes these samples for synthetic oversampling. We show that the proposed approach improves the performance of two state-of-the-art oversampling methods, namely, the synthetic minority oversampling technique and
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Erlin, Erlin, Yenny Desnelita, Nurliana Nasution, Laili Suryati, and Fransiskus Zoromi. "Dampak SMOTE terhadap Kinerja Random Forest Classifier berdasarkan Data Tidak seimbang." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 21, no. 3 (2022): 677–90. http://dx.doi.org/10.30812/matrik.v21i3.1726.

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Dalam aplikasi machine learning sangat umum ditemukan kumpulan data dalam berbagai tingkat ketidakseimbangan mulai dari ketidakseimbangan kecil, sedang sampai ekstrim. Sebagian besar model machine learning yang dilatih pada data tidak seimbang akan memiliki bias dengan memberikan tingkat akurasi yang tinggi pada kelas mayoritas dan sebaliknya rendah pada kelas minoritas. Tujuan penelitian ini adalah untuk mengevaluasi dampak dari SMOTE (Synthetic Minority Oversampling Technique) pada pengklasifikasi Random Forest untuk memprediksi penyakit jantung. Data berjumlah 299 berasal dari UCI Machine l
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Vijayvargiya, Ankit, Aparna Sinha, Naveen Gehlot, Ashutosh Jena, Rajesh Kumar, and Kieran Moran. "S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality." PLOS ONE 19, no. 5 (2024): e0301263. http://dx.doi.org/10.1371/journal.pone.0301263.

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The diagnosis of human knee abnormalities using the surface electromyography (sEMG) signal obtained from lower limb muscles with machine learning is a major problem due to the noisy nature of the sEMG signal and the imbalance in data corresponding to healthy and knee abnormal subjects. To address this challenge, a combination of wavelet decomposition (WD) with ensemble empirical mode decomposition (EEMD) and the Synthetic Minority Oversampling Technique (S-WD-EEMD) is proposed. In this study, a hybrid WD-EEMD is considered for the minimization of noises produced in the sEMG signal during the c
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Viana, Diogo, Maria Teixeira, José Baptista, and Tiago Pinto. "Synthetic minority oversampling technique for synthetic meteorological data generation*." IET Conference Proceedings 2024, no. 29 (2025): 798–802. https://doi.org/10.1049/icp.2024.4759.

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Ai, Xusheng, Jian Wu, Victor S. Sheng, Pengpeng Zhao, and Zhiming Cui. "Immune Centroids Oversampling Method for Binary Classification." Computational Intelligence and Neuroscience 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/109806.

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To improve the classification performance of imbalanced learning, a novel oversampling method, immune centroids oversampling technique (ICOTE) based on an immune network, is proposed. ICOTE generates a set of immune centroids to broaden the decision regions of the minority class space. The representative immune centroids are regarded as synthetic examples in order to resolve the imbalance problem. We utilize an artificial immune network to generate synthetic examples on clusters with high data densities, which can address the problem of synthetic minority oversampling technique (SMOTE), which
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Hanifatul Azizah, Bagus Setya Rintyarna, and Triawan Adi Cahyanto. "Sentimen Analisis Untuk Mengukur Kepercayaan Masyarakat Terhadap Pengadaan Vaksin Covid-19 Berbasis Bernoulli Naive Bayes." BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer 3, no. 1 (2022): 23–29. http://dx.doi.org/10.37148/bios.v3i1.36.

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Penelitian ini berisi tentang analisis sentimen masyarakat Indonesia pada Twitter terhadap kebijakan pemerintah dalam menangani kasus pandemi covid-19. Penelitian ini menggunakan metode Bernoulli Naive Bayes dalam melakukan pemodelan dan pengujian klasifikasi terhadap data sentimen. Digunakan juga metode pengukuran performa akurasi, presisi dan recall untuk mengukur performa metode Bernoulli Naive Bayes. Pada pembagian dan skenario pengujian digunakan teknik K Fold Cross Validation dengan nilai k = 2, 4, 5, 8 dan 10. ketidakseimbangan data dalam penelitian ini diselesaikan dengan menggunakan t
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Zhu, Tuanfei, Yaping Lin, and Yonghe Liu. "Synthetic minority oversampling technique for multiclass imbalance problems." Pattern Recognition 72 (December 2017): 327–40. http://dx.doi.org/10.1016/j.patcog.2017.07.024.

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Rozprawy doktorskie na temat "Synthetic minority oversampling technique"

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Olaitan, Olubukola. "SCUT-DS: Methodologies for Learning in Imbalanced Data Streams." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37243.

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The automation of most of our activities has led to the continuous production of data that arrive in the form of fast-arriving streams. In a supervised learning setting, instances in these streams are labeled as belonging to a particular class. When the number of classes in the data stream is more than two, such a data stream is referred to as a multi-class data stream. Multi-class imbalanced data stream describes the situation where the instance distribution of the classes is skewed, such that instances of some classes occur more frequently than others. Classes with the frequently occurring i
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Shu-WeiLiao and 廖書緯. "A Local Information Based Synthetic Minority Oversampling Technique for Imbalanced Dataset Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5mdht9.

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碩士<br>國立成功大學<br>工業與資訊管理學系<br>107<br>A dataset is imbalanced if the classes are not approximately equally represented. Data mining on imbalanced datasets receives more and more attentions in recent years. The class imbalanced problem occurs when there’s just few number of sample in one classes comparing to other classes. The SMOTE : Synthetic Minority Over-Sampling Technique is an effective method to solve imbalanced learning problem. The way is to take one of the minority sample as the seed sample, and find the minority sample nearby as the selected sample. After finding seed sample and select
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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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Części książek na temat "Synthetic minority oversampling technique"

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Zięba, Maciej, Jakub M. Tomczak, and Adam Gonczarek. "RBM-SMOTE: Restricted Boltzmann Machines for Synthetic Minority Oversampling Technique." In Intelligent Information and Database Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15702-3_37.

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Barua, Sukarna, Md Monirul Islam, and Kazuyuki Murase. "A Novel Synthetic Minority Oversampling Technique for Imbalanced Data Set Learning." In Neural Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24958-7_85.

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Patel, Vibha, Jaishree Tailor, and Amit Ganatra. "Handling Class Imbalance in Electroencephalography Data Using Synthetic Minority Oversampling Technique." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88244-0_2.

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Sreelakshmi, S., and S. S. Vinod Chandra. "Landslide Classification Using Deep Convolutional Neural Network with Synthetic Minority Oversampling Technique." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-24848-1_17.

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Subudhi, Subhashree, Ram Narayan Patro, and Pradyut Kumar Biswal. "PSO-Based Synthetic Minority Oversampling Technique for Classification of Reduced Hyperspectral Image." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1592-3_48.

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Xu, Shoukun, Zhibang Li, Baohua Yuan, Gaochao Yang, Xueyuan Wang, and Ning Li. "A No Parameter Synthetic Minority Oversampling Technique Based on Finch for Imbalanced Data." In Lecture Notes in Computer Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4752-2_31.

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De Nicolò, Francesco, Marianna La Rocca, Antonio Marrone, et al. "Time Sensitive and Oversampling Learning for Systemic Crisis Forecasting." In New Economic Windows. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64916-5_9.

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AbstractThe development of early warning systems for systemic crises has recently received growing interests. Recent studies have proposed possible solutions to address this challenging topic, in particular by means of cutting-edge artificial intelligence (AI) approaches. Financial data are fundamentally characterized by intrinsic temporal dynamics and the presence of both short-/long-term interactions. Hence, it is of paramount importance, when validating the proposed solutions to adopt validation strategies which consider this aspect. To this aim, we show here how Temporal Cross Validation (
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Zhou, Yan, Murat Kantarcioglu, and Chris Clifton. "On Improving Fairness of AI Models with Synthetic Minority Oversampling Techniques." In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977653.ch98.

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Diallo, Moussa, Abdoulaye Sidibé, and Djibril Diarra. "Imbalanced Data Classification Using Synthetic Minority Oversampling Technique in Stages for a Rice Dataset." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88226-5_25.

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Sinha, Ayush, Shubham Dwivedi, Sandeep Kumar Shukla, and O. P. Vyas. "Commissioning Random Matrix Theory and Synthetic Minority Oversampling Technique for Power System Faults Detection and Classification." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1648-1_43.

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Streszczenia konferencji na temat "Synthetic minority oversampling technique"

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Elmangoush, Abdullah M., Hanein O. Hassan, Ayyah A. Fadhl, and Malak Ahmed Alshrif. "Credit Card Fraud Detection Using Synthetic Minority Oversampling Technique and Deep Learning Technique." In 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP). IEEE, 2024. http://dx.doi.org/10.1109/atsip62566.2024.10638849.

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Islam Sajol, Md Saiful, Imtiaz Ahmed, and Quazi Sanjid Mahmud. "Synthetic Minority Oversampling Technique Enhanced Machine Learning Models for Energy Theft Detection." In 2024 IEEE Kansas Power and Energy Conference (KPEC). IEEE, 2024. http://dx.doi.org/10.1109/kpec61529.2024.10676105.

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MuhsnHasan, Montater, Meghana A, Panjagari Kavitha, T. Aditya Sai Srinivas, and P. K. Chidambaram. "Predictive Maintenance for IoT-Enabled Wireless Devices Using AdaBoost with Synthetic Minority Oversampling Technique." In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2025. https://doi.org/10.1109/icicacs65178.2025.10967754.

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Saheed, Yakub Kayode, Sulaiman Olaniyi Abdulsalam, Mohammed Babatunde Ibrahim, and Usman Ahmad Baba. "Towards a New Hybrid Synthetic Minority Oversampling Technique for Imbalanced Problem in Software Defect Prediction." In 2024 5th International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2024. https://doi.org/10.1109/icdabi63787.2024.10800331.

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Sugitha, I. Putu Yoga Tunas, Fitra Abdurrachman Bachtiar, and Satrio Agung Wicaksono. "Application of Students Graduation Prediction Model Using Decision Tree C4.5 Algorithm and Synthetic Minority Oversampling Technique (SMOTE)." In 2024 Seventh International Conference on Vocational Education and Electrical Engineering (ICVEE). IEEE, 2024. https://doi.org/10.1109/icvee63912.2024.10823806.

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Saheed, Yakub Kayode, Oluwadamilare Harazeem Abdulganiyu, Mustapha Abdulsalam, Musa Mustapha, Mekila Mbayam Olivier, and Kaloma Usman Majikumna. "A Hybrid Ant Colony Optimization for Parkinson’s Disease Classification Based on Synthetic Minority Oversampling and Adaptive Synthetic Techniques." In 2024 5th International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2024. https://doi.org/10.1109/icdabi63787.2024.10800028.

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Hossain Raju, Md Azad, Touhid Imam, Jahirul Islam, Abdullah Al Rakin, Mohammad Navid Nayyem, and Mohammad Shihab Uddin. "An Ontological Framework for Lung Carcinoma Prognostication via Sophisticated Stacking and Synthetic Minority Oversampling Techniques." In 2024 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob). IEEE, 2024. https://doi.org/10.1109/apwimob64015.2024.10792946.

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Santi, Rahmatika Pratama, Fajril Akbar, and Febby P. M. Piter. "A Comparative Study of Machine Learning Algorithm for Sentiment Analysis Using Word2Vec and Synthetic Minority Oversampling Technique (SMOTE) on COVID-19 Vaccination Program." In 2024 2nd International Symposium on Information Technology and Digital Innovation (ISITDI). IEEE, 2024. https://doi.org/10.1109/isitdi62380.2024.10796415.

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Masud, Md Abdullah Al, Alazar Araia, Yuxin Wang, Jianli Hu, and Yuhe Tian. "Machine Learning-Aided Process Design for Microwave-Assisted Ammonia Production." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.121422.

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Machine learning (ML) has become a powerful tool to analyze complex relationships between multiple variables and to unravel valuable information from big datasets. However, an open research question lies in how ML can accelerate the design and optimization of processes in the early experimental development stages with limited data. In this work, we investigate the ML-aided process design of a microwave reactor for ammonia production with exceedingly little experimental data. We propose an integrated approach of synthetic minority oversampling technique (SMOTE) regression combined with neural n
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K, Manjula Shenoy, and R. Srinivas Prabhu. "A comparative Analysis of ensemble methods and their efficiency in the classification of ‘HIT AND RUN’ cases in an imbalanced dataset (Traffic Crashes) with and without using "Synthetic minority oversampling technique"." In 2023 33rd International Conference on Computer Theory and Applications (ICCTA). IEEE, 2023. https://doi.org/10.1109/iccta60978.2023.10969264.

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Raporty organizacyjne na temat "Synthetic minority oversampling technique"

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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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