Relevant bibliographies by topics / Synthetic minority over sampling technique

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

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

Author: Grafiati
Published: 7 June 2025
Last updated: 26 June 2025

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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.

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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 sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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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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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 and converting it into balanced dataset. These techniques are (Improved Synthetic Minority Over-Sampling Technique (Improved SMOTE), Borderline-SMOTE + Imbalanced Ratio(IR), Adaptive Synthetic Sampling (ADASYN) +IR) Algorithm, where the work these techniques are generate the synthetic samples for the minority class to achieve balance between minority and majority classes and then calculate the IR between classes of minority and majority. Experimental results show ImprovedSMOTE algorithm outperform the Borderline-SMOTE + IR and ADASYN + IR algorithms because it achieves a high balance between minority and majority classes.
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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 is applied to the pre-processed data for proper data distribution, and then the properly distributed data is fed to the machine learning classifiers: Support Vector Machine (SVM), Random Forest, and Decision Tree for data classification. Experimental examination confirmed that the improved SMOTE algorithm with random forest attained significant classification results with Area under Curve (AUC) of 94.30%, 91%, 96.40%, and 99.40% on the PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases.
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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, minority over-sampling methods (such as the synthetic minority over-sampling technique and its variants) have been demonstrated to be effective for the imbalanced data classification problem. This study proposes and validates a new hypothesis with unlabeled data over-sampling, which is meaningless for imbalanced non-graph data; however, feature propagation and topological interplay mechanisms between graph nodes can facilitate the representation learning of imbalanced graphs. Furthermore, we determine empirically that ensemble data synthesis through the creation of virtual minority samples in the central region of a minority and generation of virtual unlabeled samples in the boundary region between a minority and majority is the best practice for the imbalanced multi-label graph node classification task. Our proposed novel data over-sampling framework is evaluated using multiple real-world network datasets, and it outperforms diverse, strong benchmark models by a large margin.
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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 addressing the challenge of imbalanced data in depression detection, the study aims to balance class distribution using a hybrid approach bidirectional long short-term memory (BI-LSTM) along with synthetic minority over-sampling and Tomek links and synthetic minority over-sampling and edited nearest neighbors’ techniques. This investigation presents a new approach that combines synthetic minority oversampling technique with the Kalman filter to provide an innovative extension. The Kalman-synthetic minority oversampling technique (KSMOTE) approach filters out noisy samples in the final dataset, which consists of both the original data and the artificially created samples by SMOTE. The result was greater accuracy with the BI-LSTM classification scheme compared to the other standard methods for finding depression in both unbalanced and balanced data.
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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 data platform. In this work, a novel and consistent oversampling algorithm has been proposed that can further enhance the performance of classification, especially on binary imbalanced datasets. It has been named as NMOTe (Navo Minority Oversampling Technique), an upgraded and superior alternative to the existing techniques. A critical analysis and comprehensive overview on the literature has been done to get a deeper insight into the problem statements and nurturing the need to obtain the most optimal solution. The performance of NMOTe on some standard datasets has been established in this work to get a statistical understanding on why it has edged the existing state-of-the-art to become the most robust technique for solving the two-class data imbalance problem.
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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 featuring Labuan Bajo. Results indicate that SVM without SMOTE achieves a slightly higher accuracy of 97.79% compared to 96.61% with SMOTE. However, a closer examination reveals that SVM without SMOTE accurately classifies all positive instances, while with SMOTE, one positive instance is misclassified as negative. Precision, recall, and F-measure scores for positive instances are also higher without SMOTE, indicating better performance in classifying positive sentiment
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Dissertations / Theses on the topic "Synthetic minority over sampling technique"

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Ormos, Christian. "Classification of COVID-19 Using Synthetic Minority Over-Sampling and Transfer Learning." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-430140.

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The 2019 novel coronavirus has been proven to present several unique features on chest X-rays and CT-scans that distinguish it from imaging of other pulmonary diseases such as bacterial pneumonia and viral pneumonia unrelated to COVID-19. However, the key characteristics of a COVID-19 infection have been proven challenging to detect with the human eye. The aim of this project is to explore if it is possible to distinguish a patient with COVID-19 from a patient who is not suffering from the disease from posteroanterior chest X-ray images using synthetic minority over-sampling and transfer learning. Furthermore, the report will also present the mechanics of COVID-19, the used dataset and models and the validity of the results.
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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 of major class instances might be very well, it could have a poor predictive ability to identify minority instances. Most of the risk assessment models apply sampling to deal with the class imbalanced problem. However, sampling method may lead to lack of data integrity and the model is sensitive on the sampling result as to produce inaccurate problems. This study constructs a risk model using Synthetic Minority Over-sampling Technique (SMOTE) to tackle class imbalance problems. The model we proposed not only fixed the lack of data integrity, but also solved the poor minority class predictive ability issue, hence improved the overall model accuracy. In the end, the study compares the results of classification with several sampling methods and previous Granular Computing model. By calculation and compare of the accuracy, AUC and G-means, we can conclude that using Synthetic Minority Over-sampling Technique to construct risk models would have the same or even better result than sampling and Granular Computing model.
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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 designed based on this concept. This study proposes a novel algorithm ISMOTE to solve the problem of class imbalance. ISMOTE differs from previous algorithms in that it does not take into account only a few categories of distributions, but rather measures the relative advantages of a few categories and most categories in density distributions as a basis for weighting. In addition, our approach will choose to produce artificial instances with a few category instances and most of the nearest category instances as a reference instance. This approach can reduce the situation where the classifier's learning is more difficult due to the generation of erroneous man-made data instances, and the artificial examples through this approach can better help the classifier to learn. Each of the few category instances has a weight that the classifier has difficulty studying for this data instance. The design principles of the formula are proportional to the degree of difficulty in learning with this few categories of data instances. So ISMOTE can be for each of a few categories of data instances of the weight, resulting in the corresponding number of examples of artificial data and gradually change the boundaries of classification decisions to more difficult to learn the direction.
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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 employed in this study which caused imbalanced issue. This paper presents a hybrid inference model based on the financial ratios to estimate the contractor financial performance. The proposed model is constructed by combining Synthetic Minority Over-sampling Technique (SMOTE), Least Squares Support Vector Machine (LS-SVM), and Differential Evolution (DE). In the new model, SMOTE acts imbalanced dataset problem handling of non-default and default samples, LS-SVM is used as a supervised machine learning technique for classification, and DE is employed for specifying the optimal parameter of LS-SVM. A total record of 1695 construction contractor firm-years observations from 84 non-defaulted and 28 defaulted companies is collected and used to train and validate the proposed model. The Area Under the Curve (AUC) is utilized as the performance measurement of prediction results. As shown in the experimental results, the proposed models (AUC=0.98463) outperformed the other benchmark models, including Evolutionary Support Vector Machine Inference Model (ESIM), Support Vector Machine (SVM), Artificial Neural Network (ANN), and logistic regression. Therefore, the proposed approach is a promising alternative for predicting contractor default status.
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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. To investigate the proposed algorithm, it was applied into different classified methods, such as Logistic regression, C4.5, SVM, and BPNN. In addition, this research also adopted different distance metrics to classify the same UCI datasets. The empirical results illustrated that the Euclidean distance and Manhattan distance not only perform higher percentage of accuracy rate, but also show greater computational efficiency than the Chebyshev distance and Cosine distance. Therefore, the TRkNN and SMOTE based algorithm could be widely used to handle the imbalanced datasets and how to choose the suitable distance metrics can be as the reference for the future researches. Research into cancer prediction has applied various machine learning algorithms, such as neural networks, genetic algorithms, and particle swarm optimization, to find the key to classifying illness or cancer properties or to adapt traditional statistical prediction models to effectively differentiate between different types of cancers, and thus build prediction models that can allow for early detection and treatment. Training data from existing patients is used to establish models to predict the classification accuracy of new patient samples. This issue has attracted considerable attention in the field of data mining, and scholars have proposed various methods (e.g., random sampling and feature selection) to address category imbalances and achieve a re-balanced class distribution, thus improving the effectiveness of classifiers with limited data. Although resampling methods can quickly deal with the problem of unbalanced samples, they give more importance to the data in the majority class, and neglect potentially important data in the minority class, thus limiting the effectiveness of classification. Based on patterns discovered in imbalanced medical data sets, the second task in this dissertation is to use the synthetic minority oversampling technique to improve imbalanced data set issues. In addition, this research also compares the resampling performance of various methods based on machine learning, soft-computing, and bio-inspired computing, using three UCI medical data sets.
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Book chapters on the topic "Synthetic minority over sampling technique"

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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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Patil, Sachin, and Shefali Sonavane. "Investigation of Imbalanced Big Data Set Classification: Clustering Minority Samples Over Sampling Technique." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4851-2_32.

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Puntumapon, Kamthorn, and Kitsana Waiyamai. "A Pruning-Based Approach for Searching Precise and Generalized Region for Synthetic Minority Over-Sampling." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30220-6_31.

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Duan, Yijun, Xin Liu, Adam Jatowt, et al. "Anonymity can Help Minority: A Novel Synthetic Data Over-Sampling Strategy on Multi-label Graphs." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26390-3_2.

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Daoud, Maisa, and Michael Mayo. "A Novel Synthetic Over-Sampling Technique for Imbalanced Classification of Gene Expressions Using Autoencoders and Swarm Optimization." In AI 2018: Advances in Artificial Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03991-2_55.

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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 size recurrent neural network. The study shows that our approach is competitive and efficient in classifying both intrusion and normal patterns in KDD-99 dataset.
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Conference papers on the topic "Synthetic minority over sampling technique"

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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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Zou, Yawen, Chunzhi Gu, Zi Wang, Guang Li, Jun Yu, and Chao Zhang. "Handling Class Imbalance in Black-Box Unsupervised Domain Adaptation with Synthetic Minority Over-Sampling." In 2024 IEEE International Conference on Visual Communications and Image Processing (VCIP). IEEE, 2024. https://doi.org/10.1109/vcip63160.2024.10849930.

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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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Bunkhumpornpat, Chumphol, and Sitthichoke Subpaiboonkit. "Safe level graph for synthetic minority over-sampling techniques." In 2013 13th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2013. http://dx.doi.org/10.1109/iscit.2013.6645923.

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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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Reports on the topic "Synthetic minority over sampling 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 probit model (OPM) were evaluated as statistical models, while random forest (RF) and XGBoost were evaluated as ML models. For DL, multi-layer perceptron (MLP) and TabNet were evaluated. The performance of these models varied across severity levels, with property damage only (PDO) predictions performing the best and severe injury predictions performing the worst. The TabNet model performed best in predicting severe injury and PDO crashes, while RF was the most effective in predicting moderate injury crashes. However, all models struggled with severe injury classification, indicating the potential need for model refinement and exploration of other techniques. Hence, the choice of model depends on the specific application and the relative costs of false negatives and false positives. This conclusion underscores the need for further research in this area to improve the prediction accuracy of severe and moderate injury incidents, ultimately improving available data that can be used to increase road safety.
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