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Journal articles on the topic 'HYBRID RESAMPLING'

Author: Grafiati

Published: 11 September 2023

Last updated: 31 July 2025

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1

Zhao, Lingyun, Fei Han, Qinghua Ling, et al. "Contribution-based imbalanced hybrid resampling ensemble." Pattern Recognition 164 (August 2025): 111553. https://doi.org/10.1016/j.patcog.2025.111553.

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Arun, Pattathal V., and Sunil K. Katiyar. "A CNN based Hybrid approach towards automatic image registration." Geodesy and Cartography 62, no. 1 (2013): 33–49. http://dx.doi.org/10.2478/geocart-2013-0005.

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Abstract Image registration is a key component of various image processing operations which involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however inability to properly model object shape as well as contextual information had limited the attainable accuracy. In this paper, we propose a framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), SIFT, coreset, and Cellular Autom

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Arun, Pattathal Vijayakumar. "A CNN BASED HYBRID APPROACH TOWARDS AUTOMATIC IMAGE REGISTRATION." Geodesy and Cartography 39, no. 3 (2013): 121–28. http://dx.doi.org/10.3846/20296991.2013.840409.

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Image registration is a key component of spatial analyses that involve different data sets of the same area. Automatic approaches in this domain have witnessed the application of several intelligent methodologies over the past decade; however accuracy of these approaches have been limited due to the inability to properly model shape as well as contextual information. In this paper, we investigate the possibility of an evolutionary computing based framework towards automatic image registration. Cellular Neural Network has been found to be effective in improving feature matching as well as resam

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Antonius Siagian, Novriadi, and Sardo Pardingotan Sipayung. "Handling Data Imbalance Problem in Hybrid Resampling Approach to Improve Accuracy of K-Nearest Neighbors Algorithm." Instal : Jurnal Komputer 16, no. 02 (2024): 78–87. http://dx.doi.org/10.54209/jurnalinstall.v16i02.207.

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Handling the problem of data imbalance is a crucial challenge in the development of classification models, especially in medical data such as stroke detection. This study proposes a hybrid resampling approach of SMOTE (Synthetic Minority Over-sampling Technique) and NearMiss to improve the accuracy of K-Nearest Neighbors (KNN) algorithm on stroke datasets. Our hybrid resampling approach aims to overcome the shortcomings of each resampling technique, with SMOTE generating minority class samples and NearMiss subtracting samples from the majority class. We test this approach on a stroke dataset t

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Gurcan, Fatih, and Ahmet Soylu. "Learning from Imbalanced Data: Integration of Advanced Resampling Techniques and Machine Learning Models for Enhanced Cancer Diagnosis and Prognosis." Cancers 16, no. 19 (2024): 3417. http://dx.doi.org/10.3390/cancers16193417.

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Background/Objectives: This study aims to evaluate the performance of various classification algorithms and resampling methods across multiple diagnostic and prognostic cancer datasets, addressing the challenges of class imbalance. Methods: A total of five datasets were analyzed, including three diagnostic datasets (Wisconsin Breast Cancer Database, Cancer Prediction Dataset, Lung Cancer Detection Dataset) and two prognostic datasets (Seer Breast Cancer Dataset, Differentiated Thyroid Cancer Recurrence Dataset). Nineteen resampling methods from three categories were employed, and ten classifie

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Lee, Ernesto, Furqan Rustam, Wajdi Aljedaani, Abid Ishaq, Vaibhav Rupapara, and Imran Ashraf. "Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach." Advances in Astronomy 2021 (December 3, 2021): 1–13. http://dx.doi.org/10.1155/2021/4916494.

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Pulsar stars, usually neutron stars, are spherical and compact objects containing a large quantity of mass. Each pulsar star possesses a magnetic field and emits a slightly different pattern of electromagnetic radiation which is used to identify the potential candidates for a real pulsar star. Pulsar stars are considered an important cosmic phenomenon, and scientists use them to study nuclear physics, gravitational waves, and collisions between black holes. Defining the process of automatic detection of pulsar stars can accelerate the study of pulsar stars by scientists. This study contrives a

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Zafar, Taimoor, Tariq Mairaj, Anzar Alam, and Haroon Rasheed. "Hybrid resampling scheme for particle filter-based inversion." IET Science, Measurement & Technology 14, no. 4 (2020): 396–406. http://dx.doi.org/10.1049/iet-smt.2018.5531.

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Jentsch, Carsten, and Jens-Peter Kreiss. "The multiple hybrid bootstrap — Resampling multivariate linear processes." Journal of Multivariate Analysis 101, no. 10 (2010): 2320–45. http://dx.doi.org/10.1016/j.jmva.2010.06.005.

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Saputro, Dewi Retno Sari, Sulistyaningsih Sulistyaningsih, and Purnami Widyaningsih. "SPATIAL AUTOREGRESSIVE (SAR) MODEL WITH ENSEMBLE LEARNING-MULTIPLICATIVE NOISE WITH LOGNORMAL DISTRIBUTION (CASE ON POVERTY DATA IN EAST JAVA)." MEDIA STATISTIKA 14, no. 1 (2021): 89–97. http://dx.doi.org/10.14710/medstat.14.1.89-97.

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The regression model that can be used to model spatial data is Spatial Autoregressive (SAR) model. The level of accuracy of the estimated parameters of the SAR model can be improved, especially to provide better results and can reduce the error rate by resampling method. Resampling is done by adding noise (noise) to the data using Ensemble Learning (EL) with multiplicative noise. The research objective is to estimate the parameters of the SAR model using EL with multiplicative noise. In this research was also applied a spatial regression model of the ensemble non-hybrid multiplicative noise wh

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S, Karthikeyan, and Kathirvalavakumar T. "A Hybrid Data Resampling Algorithm Combining Leader and SMOTE for Classifying the High Imbalanced Datasets." Indian Journal of Science and Technology 16, no. 16 (2023): 1214–20. https://doi.org/10.17485/IJST/v16i16.146.

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Abstract <strong>Objective:</strong> The traditional classifiers are ineffective in classifying the imbalanced datasets. Most popular approach in resolving this problem is through data re-sampling. A hybrid resampling method is proposed in this paper that reduces the misclassification in all the classes. <strong>Method:</strong> The proposed method employs the Leader algorithm for under sampling and SMOTE algorithm for oversampling. It generates the desired number of samples in both the classes based on the problem that overcomes the over-fitting and under-fitting issues. <

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Ilham, Mohamad, Adi Winarno, Moch Lutfi, and Artanti Indrasetianingsih. "Handling Imbalanced Fraudulent Transaction Data Using SMOTE-Tomek and Random Forest: A Classification Approach." BEST : Journal of Applied Electrical, Science, & Technology 7, no. 1 (2025): 35–38. https://doi.org/10.36456/best.vol7.no1.10335.

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This research aims to address the class imbalance problem in fraud detection using hybrid resampling techniques, specifically SMOTE-Tomek, combined with Random Forest classifiers. Imbalanced data in fraud detection tasks can severely hinder model performance, resulting in poor detection of minority (fraud) cases. By employing SMOTE to oversample minority class instances and Tomek links to clean the borderline majority class samples, this study evaluates the effectiveness of this hybrid method in improving classification metrics. Using a benchmark credit card fraud dataset, we compare the perfo

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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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Fadhilah, Rahmi, Heri Kuswanto, Dedy Dwi Prastyo, Dinda Ayu Safira, and M. Y. Matdoan. "COMPARISON OF RACOG AND RACOG-RUS FOR CLASSIFYING IMBALANCED DATA ON GRADIENT BOOSTING AND NAÏVE BAYES PERFORMANCE." Journal of Modern Applied Statistical Methods 24, no. 1 (2024): 89–104. https://doi.org/10.56801/jmasm.v24.i1.6.

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This study aims to determine the effect of resampling RACOG and RACOG-RUS data on Gradient Boosting and Naïve Bayes classification in predicting water quality with unbalanced data. The data used in this study were 720 data from January 2022 to December 2023. It was found that Gradient Boosting performed best when using RACOG-RUS resampling data and feature selection with a number of numIntances of 200. While Naïve Bayes has the best performance when using RACOG-RUS resampling data without feature selection with a number of numIntances of 300. It can be seen that resampling RACOG data does not

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Chen, Lingyun. "Implementing and evaluating simple resampling techniques in federated learning for imbalanced data." Applied and Computational Engineering 86, no. 1 (2024): 152–60. http://dx.doi.org/10.54254/2755-2721/86/20241578.

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Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative machine learning. However, the challenge of data imbalance, exacerbated by the non-IID nature of distributed datasets, significantly impacts model performance and fairness in FL systems. This paper investigates the implementation and evaluation of simple resampling techniques to address data imbalance within the FL framework. Using the MIMIC-III healthcare dataset, a simulated FL environment with ten virtual clients was created to test various resampling methods: SMOTE, random undersampling, and a

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Datta, Debaleena, Pradeep Kumar Mallick, Jana Shafi, Jaeyoung Choi, and Muhammad Fazal Ijaz. "Computational Intelligence for Observation and Monitoring: A Case Study of Imbalanced Hyperspectral Image Data Classification." Computational Intelligence and Neuroscience 2022 (April 30, 2022): 1–23. http://dx.doi.org/10.1155/2022/8735201.

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Imbalance in hyperspectral images creates a crisis in its analysis and classification operation. Resampling techniques are utilized to minimize the data imbalance. Although only a limited number of resampling methods were explored in the previous research, a small quantity of work has been done. In this study, we propose a novel illustrative study of the performance of the existing resampling techniques, viz. oversampling, undersampling, and hybrid sampling, for removing the imbalance from the minor samples of the hyperspectral dataset. The balanced dataset is classified in the next step, usin

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Jadwal, Pankaj Kumar, Sonal Jain, and Basant Agarwal. "Clustering-based hybrid resampling techniques for social lending data." International Journal of Intelligent Systems Technologies and Applications 20, no. 3 (2021): 183. http://dx.doi.org/10.1504/ijista.2021.10044536.

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Jadwal, Pankaj Kumar, Sonal Jain, and Basant Agarwal. "Clustering-based hybrid resampling techniques for social lending data." International Journal of Intelligent Systems Technologies and Applications 20, no. 3 (2021): 183. http://dx.doi.org/10.1504/ijista.2021.120495.

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Erianto, Ongko, and Hartono. "Hybrid approach redefinition-multi class with resampling and feature selection for multi-class imbalance with overlapping and noise." Bulletin of Electrical Engineering and Informatics 10, no. 3 (2021): pp. 1718~1728. https://doi.org/10.11591/eei.v10i3.3057.

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and accuracy of the classification. Noise must also be considered because it can reduce the performance of classification. With a resampling algorithm and feature selection, this paper proposes a method for improving the performance of hybrid approach redefinition-multi class (HAR-MI). Resampling algorithm can overcome the problem of noise but cannot handle overlapping well. Feature selection is good at dealing with overlapping but can experience a decrease in quality if there is a noise. The HAR-MI approach is a way to deal with multi-class imbalance issues, but it has some drawbacks when dea

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Shivanandappa, Manjunatha, and Malini M. Patil. "Extraction of image resampling using correlation aware convolution neural networks for image tampering detection." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 3033. http://dx.doi.org/10.11591/ijece.v12i3.pp3033-3043.

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<span>Detecting hybrid tampering attacks in an image is extremely difficult; especially when copy-clone tampered segments exhibit identical illumination and contrast level about genuine objects. The existing method fails to detect tampering when the image undergoes hybrid transformation such as scaling, rotation, compression, and also fails to detect under small-smooth tampering. The existing resampling feature extraction using the Deep learning techniques fails to obtain a good correlation among neighboring pixels in both horizontal and vertical directions. This work presents correlatio

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Manjunatha, Shivanandappa, and Shivanandappa Manjunatha. "Extraction of image resampling using correlation aware convolution neural networks for image tampering detection." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 3033–43. https://doi.org/10.11591/ijece.v12i3.pp3033-3043.

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Detecting hybrid tampering attacks in an image is extremely difficult; especially when copy-clone tampered segments exhibit identical illumination and contrast level about genuine objects. The existing method fails to detect tampering when the image undergoes hybrid transformation such as scaling, rotation, compression, and also fails to detect under small-smooth tampering. The existing resampling feature extraction using the Deep learning techniques fails to obtain a good correlation among neighboring pixels in both horizontal and vertical directions. This work presents correlation aware conv

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Ongko, Erianto, and Hartono Hartono. "Hybrid approach redefinition-multi class with resampling and feature selection for multi-class imbalance with overlapping and noise." Bulletin of Electrical Engineering and Informatics 10, no. 3 (2021): 1718–28. http://dx.doi.org/10.11591/eei.v10i3.3057.

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Class imbalance and overlapping on multi-class can reduce the performance and accuracy of the classification. Noise must also be considered because it can reduce the performance of classification. With a resampling algorithm and feature selection, this paper proposes a method for improving the performance of hybrid approach redefinition-multi class (HAR-MI). Resampling algorithm can overcome the problem of noise but cannot handle overlapping well. Feature selection is good at dealing with overlapping but can experience a decrease in quality if there is a noise. The HAR-MI approach is a way to

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22

Karthikeyan, S., and T. Kathirvalavakumar. "A Hybrid Data Resampling Algorithm Combining Leader and SMOTE for Classifying the High Imbalanced Datasets." Indian Journal Of Science And Technology 16, no. 16 (2023): 1214–20. http://dx.doi.org/10.17485/ijst/v16i16.146.

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23

Zhang, Xudong, Liang Zhao, Wei Zhong, and Feng Gu. "A novel hybrid resampling algorithm for parallel/distributed particle filters." Journal of Parallel and Distributed Computing 151 (May 2021): 24–37. http://dx.doi.org/10.1016/j.jpdc.2021.02.005.

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Ustyannie, Windyaning, Emy Setyaningsih, and Catur Iswahyudi. "Optimization of software defects prediction in imbalanced class using a combination of resampling methods with support vector machine and logistic regression." JURNAL INFOTEL 13, no. 4 (2021): 176–84. http://dx.doi.org/10.20895/infotel.v13i4.726.

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The main problem in producing high accuracy software defect prediction is if the data set has an imbalance class and dichotomous characteristics. The imbalanced class problem can be solved using a data level approach, such as resampling methods. While the problem of software defects predicting if the data set has dichotomous characteristics can be approached using the classification method. This study aimed to analyze the performance of the proposed software defect prediction method to identify the best combination of resampling methods with the appropriate classification method to provide the

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Aarthi, Velishala, V. Sri Raghavendra, V. Deekshith Rao, and Mrs Hyma Birudaraju. "Leveraging Machine Learning for Improved Detection of Medicare Fraud." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–8. https://doi.org/10.55041/ijsrem.ncft031.

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Abstract— In order to overcome imbalanced datasets in healthcare fraud detection, the effort focuses on the Medicare Part B dataset. The hybrid resampling technique (SMOTE- ENN) is used with categorical feature extraction in this unique approach to balance the dataset. For fraud detection, logistic regression is used, and performance is assessed using a variety of measures. By using this method, problems with conventional resampling techniques like noise, overfitting, and information loss are lessened. The significance of AUPRC in situations with unbalanced data is emphasised by the study. Res

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Roseline, S. Abijah, Rakesh Chandrashekar, Jothi Prabha Appadurai, et al. "Hybrid resampling technique with HWSO based temporal convolution network for credit card fraud detection." Journal of Autonomous Intelligence 7, no. 5 (2024): 1568. http://dx.doi.org/10.32629/jai.v7i5.1568.

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<p>In the wake of recent progresses in electronic trade and communiqué links, credit card use has skyrocketed for both online and in-person purchases. Maximum credit card datasets are very skewed, making it difficult to design efficient fraud detection algorithms that can help mitigate these losses. Traditional approaches are inefficient for credit card fraud detection because their architecture requires a vector to the output vector. As a result, they are unable to billet the ever-changing holders. In instruction to well recognize credit card fraud, the authors of this research suggest

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Rose Mary Mathew, Et al. "A Hybrid Resampling Approach for Multiclass Skewed Datasets and Experimental Analysis with Diverse Classifier Models." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1108–14. http://dx.doi.org/10.17762/ijritcc.v11i10.8631.

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In real-life scenarios, imbalanced datasets pose a prevalent challenge for classification tasks, where certain classes are heavily underrepresented compared to others. To combat this issue, this article introduces DOSAKU, a novel hybrid resampling technique that combines the strengths of DOSMOTE and AKCUS algorithms. By integrating both oversampling and undersampling methods, DOSAKU significantly reduces the imbalance ratio of datasets, enhancing the performance of classifiers. The proposed approach is evaluated on multiple models employing different classifiers, and the results demonstrate it

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Hartono, Hartono, and Erianto Ongko. "Avoiding Overfitting dan Overlapping in Handling Class Imbalanced Using Hybrid Approach with Smoothed Bootstrap Resampling and Feature Selection." JOIV : International Journal on Informatics Visualization 6, no. 2 (2022): 343. http://dx.doi.org/10.30630/joiv.6.2.985.

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The dataset tends to have the possibility to experience imbalance as indicated by the presence of a class with a much larger number (majority) compared to other classes(minority). This condition results in the possibility of failing to obtain a minority class even though the accuracy obtained is high. In handling class imbalance, the problems of diversity and classifier performance must be considered. Hence, the Hybrid Approach method that combines the sampling method and classifier ensembles presents satisfactory results. The Hybrid Approach generally uses the oversampling method, which is pr

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Malek, Nur Hanisah Abdul, Wan Fairos Wan Yaacob, Yap Bee Wah, Syerina Azlin Md Nasir, Norshahida Shaadan, and Sapto Wahyu Indratno. "Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (2023): 598–608. https://doi.org/10.11591/ijeecs.v29.i1.pp598-608.

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Training an imbalanced dataset can cause classifiers to overfit the majority class and increase the possibility of information loss for the minority class. Moreover, accuracy may not give a clear picture of the classifier’s performance. This paper utilized decision tree (DT), support vector machine (SVM), artificial neural networks (ANN), K-nearest neighbors (KNN) and Naïve Bayes (NB) besides ensemble models like random forest (RF) and gradient boosting (GB), which use bagging and boosting methods, three sampling approaches and seven performance metrics to investigate the effect of

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Jimmy, alexander Cortés Osorio, Andrés Chaves Osorio José, and David López Robayo Cristian. "Hybrid algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors." Revista Facultad de Ingeniería, Universidad de Antioquia, no. 105 (November 2, 2021): 111–21. https://doi.org/10.17533/udea.redin.20211165.

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Today, image forgery is common due to the massification of low-cost/high-resolution digital cameras, along with the accessibility of computer programs for image processing. All media is affected by this issue, which makes the public doubt the news. Though image modification is a typical process in entertainment, when images are taken as evidence in a legal process, modification cannot be considered trivial. Digital forensics has the challenge of ensuring the accuracy and integrity of digital images to overcome this issue. This investigation introduces an algorithm to detect the main types of p

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Snieder, Everett, Karen Abogadil, and Usman T. Khan. "Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy." Hydrology and Earth System Sciences 25, no. 5 (2021): 2543–66. http://dx.doi.org/10.5194/hess-25-2543-2021.

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Abstract. Data-driven flow-forecasting models, such as artificial neural networks (ANNs), are increasingly featured in research for their potential use in operational riverine flood warning systems. However, the distributions of observed flow data are imbalanced, resulting in poor prediction accuracy on high flows in terms of both amplitude and timing error. Resampling and ensemble techniques have been shown to improve model performance on imbalanced datasets. However, the efficacy of these methods (individually or combined) has not been explicitly evaluated for improving high-flow forecasts.

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Zhao, Zixue, Tianxiang Cui, Shusheng Ding, Jiawei Li, and Anthony Graham Bellotti. "Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction." Mathematics 12, no. 5 (2024): 701. http://dx.doi.org/10.3390/math12050701.

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Credit risk prediction heavily relies on historical data provided by financial institutions. The goal is to identify commonalities among defaulting users based on existing information. However, data on defaulters is often limited, leading to a concentration of credit data where positive samples (defaults) are significantly fewer than negative samples (nondefaults). It poses a serious challenge known as the class imbalance problem, which can substantially impact data quality and predictive model effectiveness. To address the problem, various resampling techniques have been proposed and studied

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A, 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.

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The realm of healthcare fraud detection is continually changing and encounters substantial obstacles, especially when dealing with data imbalance problems. Earlier research primarily concentrated on standard machine learning (ML) methods, which often have difficulty with imbalanced data. This issue manifests in several ways. It involves the danger of overfitting with Random Oversampling (ROS), the creation of noise by the Synthetic Minority Oversampling Technique (SMOTE), and the possible loss of vital information with Random Undersampling (RUS). Furthermore, enhancing model performance, exami

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Shyam, P. "Credit Card Fraud Detection Using Ensemble (Stacking and Voting Classifiers) with Hybrid Techniques." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 6555–65. https://doi.org/10.22214/ijraset.2025.71710.

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Credit card fraud remains a critical challenge in the financial industry due to the highly imbalanced nature of fraud detection datasets and the evolving tactics of fraudsters. This study proposes a robust framework for Credit Card Fraud Detection Using Ensemble (Stacking and Voting Classifiers) with Hybrid Techniques, integrating advanced resampling strategies with ensemble learning to enhance the detection of minority fraud cases.We evaluated various machine learning models combined with hybrid oversampling and undersampling methods, including Simple Minority Oversampling Technique(SMOTE)-To

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Seetan, Raed, Jacob Bible, Michael Karavias, Wael Seitan, and Sam Thangiah. "Radiation Hybrid Mapping: A Resampling-based Method for Building High-Resolution Maps." Advances in Science, Technology and Engineering Systems Journal 2, no. 3 (2017): 1390–400. http://dx.doi.org/10.25046/aj0203175.

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Hameed, Faisal, Sumesh Manjunath Ramesh, and Hoda Alkhzaimi. "Improved Hybrid Bagging Resampling Framework for Deep Learning-Based Side-Channel Analysis." Computers 13, no. 8 (2024): 210. http://dx.doi.org/10.3390/computers13080210.

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As cryptographic implementations leak secret information through side-channel emissions, the Hamming weight (HW) leakage model is widely used in deep learning profiling side-channel analysis (SCA) attacks to expose the leaked model. However, imbalanced datasets often arise from the HW leakage model, increasing the attack complexity and limiting the performance of deep learning-based SCA attacks. Effective management of class imbalance is vital for training deep neural network models to achieve optimized and improved performance results. Recent works focus on either improved deep-learning metho

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Cao, Lu, and Hong Shen. "Imbalanced data classification based on hybrid resampling and twin support vector machine." Computer Science and Information Systems 14, no. 3 (2017): 579–95. http://dx.doi.org/10.2298/csis161221017l.

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Imbalanced datasets exist widely in real life. The identification of the minority class in imbalanced datasets tends to be the focus of classification. As a variant of enhanced support vector machine (SVM), the twin support vector machine (TWSVM) provides an effective technique for data classification. TWSVM is based on a relative balance in the training sample dataset and distribution to improve the classification accuracy of the whole dataset, however, it is not effective in dealing with imbalanced data classification problems. In this paper, we propose to combine a re-sampling technique, wh

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Malek, Nur Hanisah Abdul, Wan Fairos Wan Yaacob, Yap Bee Wah, Syerina Azlin Md Nasir, Norshahida Shaadan, and Sapto Wahyu Indratno. "Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (2022): 598. http://dx.doi.org/10.11591/ijeecs.v29.i1.pp598-608.

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Training an imbalanced dataset can cause classifiers to overfit the majority class and increase the possibility of information loss for the minority class. Moreover, accuracy may not give a clear picture of the classifier’s performance. This paper utilized decision tree (DT), support vector machine (SVM), artificial neural networks (ANN), K-nearest neighbors (KNN) and Naïve Bayes (NB) besides ensemble models like random forest (RF) and gradient boosting (GB), which use bagging and boosting methods, three sampling approaches and seven performance metrics to investigate the effect of class imbal

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Werner, Mirco, Vincent Schüßler, and Carsten Dachsbacher. "ReSTIR Subsurface Scattering for Real-Time Path Tracing." Proceedings of the ACM on Computer Graphics and Interactive Techniques 7, no. 3 (2024): 1–19. http://dx.doi.org/10.1145/3675372.

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Subsurface scattering is an important visual cue and in real-time rendering it is often approximated using screen-space algorithms. Path tracing with the diffusion approximation can easily overcome the limitations of these algorithms, but increases image noise. We improve its efficiency by applying reservoir-based spatiotemporal importance resampling (ReSTIR) to subsurface light transport paths. For this, we adopt BSSRDF importance sampling for generating candidates. Further, spatiotemporal reuse requires shifting paths between domains. We observe that different image regions benefit most from

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Vangaru, Uday Sai Kiran. "Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image and Video Forgeries." International Journal for Research in Applied Science and Engineering Technology 12, no. 10 (2024): 105–7. http://dx.doi.org/10.22214/ijraset.2024.64450.

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This paper introduces a hybrid architecture combining Long Short-Term Memory (LSTM) networks and an encoderdecoder model for the detection and localization of image and video forgeries. The proposed system leverages resampling features and LSTM cells to identify manipulation patterns such as splicing and retouching in multimedia content. By utilizing a combination of spatial and temporal features, the model achieves high precision in detecting forged regions. Extensive testing on diverse datasets demonstrates the robustness of the proposed method.

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Wang, Xiaohui, Hao Zhang, Shengzhou Bai, and Yuxian Yue. "Design of agile satellite constellation based on hybrid-resampling particle swarm optimization method." Acta Astronautica 178 (January 2021): 595–605. http://dx.doi.org/10.1016/j.actaastro.2020.09.040.

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Barhdadi, M., B. Benyacoub, and M. Ouzineb. "A hybrid variable neighborhood search with bootstrap resampling technique for credit scoring problem." Mathematical Modeling and Computing 11, no. 1 (2024): 109–19. http://dx.doi.org/10.23939/mmc2024.01.109.

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Credit scoring models have played a vitally important role in the granting credit by lenders and financial institutions. Recently, these have gained more attention related to the risk management practice. Many modeling techniques have been developed to evaluate the worthiness of borrowers. This paper presents a credit scoring model via one of local search methods – variable neighborhood search (VNS) algorithm. The optimizing VNS neighborhood structure is a useful method applied to solve credit scoring problems. By simultaneously tuning the neighborhood structure, the proposed algorithm generat

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Ersa Budi Sutanto, Ghytsa Alif Jabir, Nadhifan Humam Fitrial, Ni Luh Putu Yayang Septia Ningsih, Siti Andhasah Siti Andhasah, and Rani Nooraeni. "Faktor-Faktor yang Memengaruhi Pernikahan Dini pada Wanita Usia 20-24 di Indonesia Tahun 2017: Penerapan Metode Regresi Logistik Biner dengan Penyesuaian Resampling Data Imbalance." Jurnal Statistika dan Aplikasinya 3, no. 1 (2019): 39–49. http://dx.doi.org/10.21009/jsa.03105.

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Pernikahan dini adalah perkawinan yang terjadi pada anak di bawah usia 18 tahun. Secara umum, angka prevalensi pernikahan dini di Indonesia masih cukup tinggi karena 23 dari 34 provinsi di Indonesia memiliki angka prevalensi pernikahan pada usia dini diatas rata-rata nasional. Kategori yang digunakan pada kasus ini menunjukkan keadaan imbalance sehingga diperlukan adanya penyesuaian dalam menganalisis data. Permasalahan yang sering dijumpai akses data kasus pernikahan dini tidak tercatat atau terekam pada dokumen resmi. Dalam upaya untuk mengatasi hal tersebut, penelitian ini menggunakan penga

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Nieto-del-Amor, Félix, Gema Prats-Boluda, Javier Garcia-Casado, et al. "Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data." Sensors 22, no. 14 (2022): 5098. http://dx.doi.org/10.3390/s22145098.

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Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models’ real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the train

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Wongvorachan, Tarid, Surina He, and Okan Bulut. "A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining." Information 14, no. 1 (2023): 54. http://dx.doi.org/10.3390/info14010054.

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Educational data mining is capable of producing useful data-driven applications (e.g., early warning systems in schools or the prediction of students’ academic achievement) based on predictive models. However, the class imbalance problem in educational datasets could hamper the accuracy of predictive models as many of these models are designed on the assumption that the predicted class is balanced. Although previous studies proposed several methods to deal with the imbalanced class problem, most of them focused on the technical details of how to improve each technique, while only a few focused

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Wang, Qiang. "A Hybrid Sampling SVM Approach to Imbalanced Data Classification." Abstract and Applied Analysis 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/972786.

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Imbalanced datasets are frequently found in many real applications. Resampling is one of the effective solutions due to generating a relatively balanced class distribution. In this paper, a hybrid sampling SVM approach is proposed combining an oversampling technique and an undersampling technique for addressing the imbalanced data classification problem. The proposed approach first uses an undersampling technique to delete some samples of the majority class with less classification information and then applies an oversampling technique to gradually create some new positive samples. Thus, a bal

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Restrepo, John, Nelson Correa-Rojas, and Jorge Herrera-Ramirez. "Speckle Noise Reduction in Digital Holography Using a DMD and Multi-Hologram Resampling." Applied Sciences 10, no. 22 (2020): 8277. http://dx.doi.org/10.3390/app10228277.

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Speckle noise is a well-documented problem on coherent imaging techniques like Digital Holography. A method to reduce the speckle noise level is presented, based on introducing a Digital Micromirror Device to phase modulate the illumination over the object. Multiple holograms with varying illuminations are recorded and the reconstructed intensities are averaged to obtain a final improved image. A simple numerical resampling scheme is proposed to further improve noise reduction. The obtained results demonstrate the effectiveness of the hybrid approach.

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Haberlandt, U., A. D. Ebner von Eschenbach, and I. Buchwald. "A space-time hybrid hourly rainfall model for derived flood frequency analysis." Hydrology and Earth System Sciences Discussions 5, no. 4 (2008): 2459–90. http://dx.doi.org/10.5194/hessd-5-2459-2008.

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Abstract. For derived flood frequency analysis based on hydrological modelling long continuous precipitation time series with high temporal resolution are needed. Often, the observation network with recording rainfall gauges is poor, so stochastic precipitation synthesis is a good alternative. Here, a hybrid two step procedure is proposed to provide suitable space-time precipitation fields as input for hydrological modelling. First, a univariate alternating renewal model is presented to simulate independent hourly precipitation time series for several locations. In the second step a multi-site

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Najeeb, Miftah Asharaf, and Alhaam Alariyibi. "Imbalanced Dataset Effect on CNN-Based Classifier Performance for Face Recognition." International Journal of Artificial Intelligence & Applications 15, no. 1 (2024): 25–41. http://dx.doi.org/10.5121/ijaia.2024.15102.

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Facial Recognition is integral to numerous modern applications, such as security systems, social media platforms, and augmented reality apps. The success of these systems heavily depends on the performance of the Face Recognition models they use, specifically Convolutional Neural Networks (CNNs). However, many real-world classification tasks encounter imbalanced datasets, with some classes significantly underrepresented. Face Recognition models that do not address this class imbalance tend to exhibit poor performance, especially in tasks involving a wide range of faces to identify (multi-class

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Sukamto, Hadiyanto, and Kurnianingsih. "A Hybrid Resampling Method with K-Nearest Neighbour (FHR-KNN) for Imbalanced Preeclampsia Dataset." Ingénierie des systèmes d information 28, no. 2 (2023): 483–90. http://dx.doi.org/10.18280/isi.280225.

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