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Journal articles on the topic 'Resampling Techniques'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

KENDERDINE, RICHARD D. "WAVELET-BASED RESAMPLING TECHNIQUES." Bulletin of the Australian Mathematical Society 85, no. 2 (2012): 351–52. http://dx.doi.org/10.1017/s0004972711003303.

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S Malini, Manjunatha, and M. Patil. "Interpolation Techniques in Image Resampling." International Journal of Engineering & Technology 7, no. 3.34 (2018): 567. http://dx.doi.org/10.14419/ijet.v7i3.34.19383.

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The procedure of converting a sampled image from any coordinate structure to other structure is called Image Resampling. When forgeries are introduced in digital images, generally the operations like rotation, resizing, skewing etc., are included to make it relational with respect to adjacent original area. So there is a recognizable loss in the quality of the image and it will become an important signature of manipulated images. Hence resampling is the default interpretation present in most of the tamped image. Resampling detection is an attractive standard tool in digital image forensics. Ge

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Indrawati, Ariani, Hendro Subagyo, Andre Sihombing, Wagiyah Wagiyah, and Sjaeful Afandi. "ANALYZING THE IMPACT OF RESAMPLING METHOD FOR IMBALANCED DATA TEXT IN INDONESIAN SCIENTIFIC ARTICLES CATEGORIZATION." BACA: JURNAL DOKUMENTASI DAN INFORMASI 41, no. 2 (2020): 133. http://dx.doi.org/10.14203/j.baca.v41i2.702.

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The extremely skewed data in artificial intelligence, machine learning, and data mining cases are often given misleading results. It is caused because machine learning algorithms are designated to work best with balanced data. However, we often meet with imbalanced data in the real situation. To handling imbalanced data issues, the most popular technique is resampling the dataset to modify the number of instances in the majority and minority classes into a standard balanced data. Many resampling techniques, oversampling, undersampling, or combined both of them, have been proposed and continue

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Muzayanah, Rini, Apri Dwi Lestari, Jumanto Jumanto, Budi Prasetiyo, Dwika Ananda Agustina Pertiwi, and Much Aziz Muslim. "Comparative Study of Imbalanced Data Oversampling Techniques for Peer-to-Peer Landing Loan Prediction." Scientific Journal of Informatics 11, no. 1 (2024): 245–54. http://dx.doi.org/10.15294/sji.v11i1.50274.

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Purpose: Data imbalances that often occur in the classification of loan data on the Peer-to-Peer Lending platform cancause algorithm performance to be less than optimal, causing the resulting accuracy to decrease. To overcome thisproblem, appropriate resampling techniques are needed so that the classification algorithm can work optimally andprovide results with optimal accuracy. This research aims to find the right resampling technique to overcome theproblem of data imbalance in data lending on peer-to-peer landing platforms.Methods: This study uses the XGBoost classification algorithm to eval

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Mittas, Nikolaos, and Lefteris Angelis. "Comparing cost prediction models by resampling techniques." Journal of Systems and Software 81, no. 5 (2008): 616–32. http://dx.doi.org/10.1016/j.jss.2007.07.039.

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Alonso, Andrés M., Daniel Peña, and Juan Romo. "Resampling time series using missing values techniques." Annals of the Institute of Statistical Mathematics 55, no. 4 (2003): 765–96. http://dx.doi.org/10.1007/bf02523392.

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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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Simmachan, Teerawat, and Pichit Boonkrong. "Effect of Resampling Techniques on Machine Learning Models for Classifying Road Accident Severity in Thailand." Journal of Current Science and Technology 15, no. 2 (2025): 99. https://doi.org/10.59796/jcst.v15n2.2025.99.

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Road traffic accidents (RTAs) pose a significant global challenge, particularly in Thailand. This study investigates the impact of resampling techniques on machine learning (ML) models for classifying road accident severity in Thailand, utilizing data from 31,817 road traffic accidents collected between January 1, 2021, and December 31, 2022. The primary challenge addressed is class imbalance, where fatal accidents represent a small fraction of the dataset. Three popular ML models, including Random Forest (RF), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGB), were evaluated with

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PARMANTO, BAMBANG, PAUL W. MUNRO, and HOWARD R. DOYLE. "Reducing Variance of Committee Prediction with Resampling Techniques." Connection Science 8, no. 3-4 (1996): 405–26. http://dx.doi.org/10.1080/095400996116848.

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Kasmi, Chaouki, Sebastien Lallechere, Jose Lopes Esteves, et al. "Stochastic EMC/EMI Experiments Optimization Using Resampling Techniques." IEEE Transactions on Electromagnetic Compatibility 58, no. 4 (2016): 1143–50. http://dx.doi.org/10.1109/temc.2016.2557847.

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Vafeiadis, Thanasis, Efthimia Bora-Senta, and Dimitris Kugiumtzis. "Evaluation of Linear Trend Tests Using Resampling Techniques." Communications in Statistics - Simulation and Computation 37, no. 5 (2008): 907–23. http://dx.doi.org/10.1080/03610910701858371.

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Li, Xinyi. "Improved Logistic Regression Model Based on Resampling Techniques." Highlights in Science, Engineering and Technology 136 (March 31, 2025): 28–36. https://doi.org/10.54097/dnbeak17.

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When facing imbalanced samples and high-dimensional features, the performance of traditional logistic regression models may significantly deteriorate, even becoming completely ineffective. Therefore, this paper proposes an improved logistic regression method combined with resampling techniques. Firstly, the proposed method uses the "resampling and encoding" strategy to effectively capture the predictive information that has a significant impact on the classification result while addressing the problems of imbalanced samples and dimension curse. Secondly, the proposed method uses weighted combi

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Ramos-Pérez, Ismael, José Antonio Barbero-Aparicio, Antonio Canepa-Oneto, Álvar Arnaiz-González, and Jesús Maudes-Raedo. "An Extensive Performance Comparison between Feature Reduction and Feature Selection Preprocessing Algorithms on Imbalanced Wide Data." Information 15, no. 4 (2024): 223. http://dx.doi.org/10.3390/info15040223.

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The most common preprocessing techniques used to deal with datasets having high dimensionality and a low number of instances—or wide data—are feature reduction (FR), feature selection (FS), and resampling. This study explores the use of FR and resampling techniques, expanding the limited comparisons between FR and filter FS methods in the existing literature, especially in the context of wide data. We compare the optimal outcomes from a previous comprehensive study of FS against new experiments conducted using FR methods. Two specific challenges associated with the use of FR are outlined in de

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Kowalewski, Michał, and Phil Novack-Gottshall. "Resampling Methods in Paleontology." Paleontological Society Papers 16 (October 2010): 19–54. http://dx.doi.org/10.1017/s1089332600001807.

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This chapter reviews major types of statistical resampling approaches used in paleontology. They are an increasingly popular alternative to the classic parametric approach because they can approximate behaviors of parameters that are not understood theoretically. The primary goal of most resampling methods is an empirical approximation of a sampling distribution of a statistic of interest, whether simple (mean or standard error) or more complicated (median, kurtosis, or eigenvalue). This chapter focuses on the conceptual and practical aspects of resampling methods that a user is likely to face

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Del Giudice, Vincenzo, Francesca Salvo, and Pierfrancesco De Paola. "Resampling Techniques for Real Estate Appraisals: Testing the Bootstrap Approach." Sustainability 10, no. 9 (2018): 3085. http://dx.doi.org/10.3390/su10093085.

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Applied to real estate markets analysis, the resampling methods aim to contribute to the knowledge growth of real estate market dynamics, overcoming the issues related to the data scarcity and operational limits of traditional statistical theory. Among resampling methods, the Bootstrap technique appears to be the most suitable for the interpretation of real estate phenomena. In this study, for residential properties located in Cosenza (Calabria Region, Italy), a Bootstrap approach has been used in order to determine the marginal prices of the real estate characteristics detected, comparing the

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Cazelles, Bernard, Kévin Cazelles, and Mario Chavez. "Wavelet analysis in ecology and epidemiology: impact of statistical tests." Journal of The Royal Society Interface 11, no. 91 (2014): 20130585. http://dx.doi.org/10.1098/rsif.2013.0585.

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Wavelet analysis is now frequently used to extract information from ecological and epidemiological time series. Statistical hypothesis tests are conducted on associated wavelet quantities to assess the likelihood that they are due to a random process. Such random processes represent null models and are generally based on synthetic data that share some statistical characteristics with the original time series. This allows the comparison of null statistics with those obtained from original time series. When creating synthetic datasets, different techniques of resampling result in different chara

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Gonzalez, Mailen, José Manuel Fuertes García, María Belén Zanchetta, Rubén Abdala, and José María Massa. "Comparison of Resampling Methods and Radiomic Machine Learning Classifiers for Predicting Bone Quality Using Dual-Energy X-Ray Absorptiometry." Diagnostics 15, no. 2 (2025): 175. https://doi.org/10.3390/diagnostics15020175.

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Background/Objectives: This study presents a novel approach, based on a combination of radiomic feature extraction, data resampling techniques, and machine learning algorithms, for the detection of degraded bone structures in Dual X-ray Absorptiometry (DXA) images. This comprehensive approach, which addresses the critical aspects of the problem, distinguishes this work from previous studies, improving the performance achieved by the most similar studies. The primary aim is to provide clinicians with an accessible tool for quality bone assessment, which is currently limited. Methods: A dataset

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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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Constancio, Elven, and Ken Ditha Tania. "Penerapan Metode Supervised Learning dan Teknik Resampling untuk Prediksi Penipuan Transaksi Keuangan." Building of Informatics, Technology and Science (BITS) 6, no. 3 (2024): 1427–39. https://doi.org/10.47065/bits.v6i3.6110.

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Financial transaction fraud can result in devastating consequences for the stability of companies, as well as huge losses for shareholders, the industry, and even the market as a whole. As fraud in financial transactions increases, there is a need for effective methods to accurately detect and prevent fraudulent activities. This study aims to compare the performance of five machine learning models, namely Random Forest, K-Nearest Neighbors (KNN), Decision Tree, XGBoost, and Extra Trees, in detecting financial transaction fraud using an imbalanced dataset. To overcome the data imbalance problem

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20

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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21

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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Fitrianto, Anwar, and Punitha Linganathan. "Comparisons between Resampling Techniques in Linear Regression: A Simulation Study." CAUCHY: Jurnal Matematika Murni dan Aplikasi 7, no. 3 (2022): 345–53. http://dx.doi.org/10.18860/ca.v7i3.14550.

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The classic methods used in estimating the parameters in linear regression need to fulfill some assumptions. If the assumptions are not fulfilled, the conclusion is questionable. Resampling is one of the ways to avoid such problems. The study aims to compare resampling techniques in linear regression. The original data used in the study is clean, without any influential observations, outliers and leverage points. The ordinary least square method was used as the primary method to estimate the parameters and then compared with resampling techniques. The variance, p-value, bias, and standard erro

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Arisandi, Rizwan. "PERBANDINGAN MODEL KLASIFIKASI RANDOM FOREST DENGAN RESAMPLING DAN TANPA RESAMPLING PADA PASIEN PENDERITA GAGAL JANTUNG." Jurnal Gaussian 12, no. 1 (2023): 136–45. http://dx.doi.org/10.14710/j.gauss.12.1.136-145.

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Cardiovascular disease that causes heart failure is one of the diseases with the highest mortality rate in the world. Therefore, there is a need for an accurate model to classify heart failure based on clinical information and the lifestyle of patients with the disease, as an alternative solution in administering appropriate drugs. This study compared the classification model of living and deceased heart failure patients based on clinical information and patient lifestyle using the random forest method when using resampling techniques and not using resampling techniques. The results obtained f

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Weber, Keith T., and Jackie Langille. "Improving Classification Accuracy Assessments with Statistical Bootstrap Resampling Techniques." GIScience & Remote Sensing 44, no. 3 (2007): 237–50. http://dx.doi.org/10.2747/1548-1603.44.3.237.

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Cordeiro, Clara, and Maria Manuela Neves. "Exponential smoothing and resampling techniques in time series prediction." Discussiones Mathematicae Probability and Statistics 30, no. 1 (2010): 87. http://dx.doi.org/10.7151/dmps.1122.

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Amelia, Tri Suci, Mila Nirmala Sari Hasibuan, and Rahmadani Pane. "Comparative analysis of resampling techniques on Machine Learning algorithm." Sinkron 7, no. 2 (2022): 628–34. http://dx.doi.org/10.33395/sinkron.v7i2.11427.

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Generally, classification algorithms in the field of data science assume that the classes of training data are equally distributed. However, datasets on real problems often have an unbalanced class distribution. Unbalanced dataset classes make up the majority class and the minority class. In general, minority classes are more attractive and more important to identify. In this case, the correct classification for the minority class sample is more valuable than the majority class. The unbalanced class distribution causes the classification algorithm to have difficulty in classifying minority cla

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Kuptametee, Chanin, and Nattapol Aunsri. "A review of resampling techniques in particle filtering framework." Measurement 193 (April 2022): 110836. http://dx.doi.org/10.1016/j.measurement.2022.110836.

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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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Subhiyakto, Egia Rosi, Sindhu Rakasiwi, Junta Zeniarja, et al. "Evaluation of Resampling Techniques in CNN-Based Heartbeat Classification." Ingénierie des systèmes d information 29, no. 4 (2024): 1323–32. http://dx.doi.org/10.18280/isi.290408.

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Hall, M. J., H. F. P. van den Boogaard, R. C. Fernando, and A. E. Mynett. "The construction of confidence intervals for frequency analysis using resampling techniques." Hydrology and Earth System Sciences 8, no. 2 (2004): 235–46. http://dx.doi.org/10.5194/hess-8-235-2004.

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Abstract. Resampling techniques such as the Bootstrap and the Jack-knife are generic methods for the estimation of uncertainties in statistics. When applied in frequency analysis, resampling techniques can provide estimates of the uncertainties in both distribution parameters and quantile estimates in circumstances in which confidence limits cannot be obtained theoretically. Test experiments using two different parameter estimation methods on two types of distributions with different initial sample sizes and numbers of resamples has confirmed the utility of such methods. However, care is neces

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Jiang, Juan Fen, Yue Qi Zhong, and Qiu Ping Zhang. "Three-Dimensional Garment Surface Reconstruction Based on Ball-Pivoting Algorithm." Advanced Materials Research 821-822 (September 2013): 765–68. http://dx.doi.org/10.4028/www.scientific.net/amr.821-822.765.

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After 3D garment surface resampling, the topological graph of the resampling points should be reconstructed, that is a new triangular mesh can be generated. We exploit automatic method to generate topology of the mesh with total number of resampling points. In our approach, based and ball-pivoting algorithm based surface reconstruction techniques are introduced respectively, has an accurate shape approximated the original resampling points cloud, but exists large unwanted components. The ball-pivoting algorithm is selected to reconstruct the topological graph, as the principle is simple, the b

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Nassr, Zineb, Faouzia Benabbou, Nawal Sael, and Touria Hamim. "Improving Sentiment Analysis Performance on Imbalanced Moroccan Dialect Datasets Using Resample and Feature Extraction Techniques." Information 16, no. 1 (2025): 39. https://doi.org/10.3390/info16010039.

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Sentiment analysis is a crucial component of text mining and natural language processing (NLP), involving the evaluation and classification of text data based on its emotional tone, typically categorized as positive, negative, or neutral. While significant research has focused on structured languages like English, unstructured languages, such as the Moroccan Dialect (MD), face substantial resource limitations and linguistic challenges, making effective sentiment analysis difficult. This study addresses this gap by exploring the integration of data-balancing techniques with machine learning (ML

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Hasan, Syed Sarfaraz, Arun Baitha, Lal Singh Gangwar, and Sanjeev Kumar. "Evaluation of resampling techniques for deep learning based identification of promising genotypes in sugarcane varietal trials." Indian Journal of Genetics and Plant Breeding (The) 84, no. 01 (2024): 92–98. http://dx.doi.org/10.31742/isgpb.84.1.8.

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Deep learning is a class of machine learning algorithms that extract high-level features from the raw input for making intelligent decisions. Identification of promising genotypes in varietal trials is one of many agriculture domain applications requiring implementation of deep learning to perform intelligent decision using varietal trial data. However, it has been found that varietal trial data to be used for identification is highly imbalanced one providing great challenges for classification tasks in deep learning. For example, only 33 genotypes were identified as promising in zonal varieta

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Harrison, Matthew T. "Accelerated Spike Resampling for Accurate Multiple Testing Controls." Neural Computation 25, no. 2 (2013): 418–49. http://dx.doi.org/10.1162/neco_a_00399.

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Controlling for multiple hypothesis tests using standard spike resampling techniques often requires prohibitive amounts of computation. Importance sampling techniques can be used to accelerate the computation. The general theory is presented, along with specific examples for testing differences across conditions using permutation tests and for testing pairwise synchrony and precise lagged-correlation between many simultaneously recorded spike trains using interval jitter.

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Lin, Daqi, Chris Wyman, and Cem Yuksel. "Fast volume rendering with spatiotemporal reservoir resampling." ACM Transactions on Graphics 40, no. 6 (2021): 1–18. http://dx.doi.org/10.1145/3478513.3480499.

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Volume rendering under complex, dynamic lighting is challenging, especially if targeting real-time. To address this challenge, we extend a recent direct illumination sampling technique, spatiotemporal reservoir resampling, to multi-dimensional path space for volumetric media. By fully evaluating just a single path sample per pixel, our volumetric path tracer shows unprecedented convergence. To achieve this, we properly estimate the chosen sample's probability via approximate perfect importance sampling with spatiotemporal resampling. A key observation is recognizing that applying cheaper, bias

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Zhi, Rui Rui, Tian Cheng Li, Ming Fei Siyau, and Shu Dong Sun. "Applied Technology in Adapting the Number of Particles while Maintaining the Diversity in the Particle Filter." Advanced Materials Research 951 (May 2014): 202–7. http://dx.doi.org/10.4028/www.scientific.net/amr.951.202.

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Determining the required number of particles is a challenging task toward the application of particle filters (PF). As smaller number of particles means faster computing speed while larger number of particles means better approximation ability, it is vital to balance the trade-off between computing speed and approximation quality. Moreover, to match the system requirement that often varies in time, the number of particles should be adapted in time. To achieve these, an Adaptive Deterministic Resampling (ADR) is proposed in this paper. The new resampling employs techniques combine Deterministic

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Hu, Zhongyi, Raymond Chiong, Ilung Pranata, Yukun Bao, and Yuqing Lin. "Malicious web domain identification using online credibility and performance data by considering the class imbalance issue." Industrial Management & Data Systems 119, no. 3 (2019): 676–96. http://dx.doi.org/10.1108/imds-02-2018-0072.

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Purpose Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones). Design/methodology/approach The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO)

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Ciciana, Ciciana, Rahmawati Rahmawati, and Laila Qadrini. "The Utilization of Resampling Techniques and the Random Forest Method in Data Classification." TIN: Terapan Informatika Nusantara 4, no. 4 (2023): 252–59. http://dx.doi.org/10.47065/tin.v4i4.4342.

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In data classification, there are various methods that can be employed, one of which is the random forest method. This method proves effective in handling non-linear data, exhibiting robustness against extreme data points and disturbances, and providing ease of use that results in high-quality classification outcomes. Data imbalance, where one class has more or fewer instances than the others, is a common issue. In situations of data imbalance, most classification models tend to favor the majority class, which can lead to overfitting and unsatisfactory classification results. To address this i

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Doran, Gary, and Soumya Ray. "SMILe: Shuffled Multiple-Instance Learning." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 260–66. http://dx.doi.org/10.1609/aaai.v27i1.8651.

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Resampling techniques such as bagging are often used in supervised learning to produce more accurate classifiers. In this work, we show that multiple-instance learning admits a different form of resampling, which we call "shuffling." In shuffling, we resample instances in such a way that the resulting bags are likely to be correctly labeled. We show that resampling results in both a reduction of bag label noise and a propagation of additional informative constraints to a multiple-instance classifier. We empirically evaluate shuffling in the context of multiple-instance classification and multi

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Kashongwe, Olivier, Tina Kabelitz, Christian Ammon, et al. "Influence of Preprocessing Methods of Automated Milking Systems Data on Prediction of Mastitis with Machine Learning Models." AgriEngineering 6, no. 3 (2024): 3427–42. http://dx.doi.org/10.3390/agriengineering6030195.

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Missing data and class imbalance hinder the accurate prediction of rare events such as dairy mastitis. Resampling and imputation are employed to handle these problems. These methods are often used arbitrarily, despite their profound impact on prediction due to changes caused to the data structure. We hypothesize that their use affects the performance of ML models fitted to automated milking systems (AMSs) data for mastitis prediction. We compare three imputations—simple imputer (SI), multiple imputer (MICE) and linear interpolation (LI)—and three resampling techniques: Synthetic Minority Overs

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Masturoh, Siti, Fitra Septia Nugraha, Siti Nurlela, M. Rangga Ramadhan Saelan, Daniati Uki Eka Saputri, and Ridan Nurfalah. "TELEMARKETING BANK SUCCESS PREDICTION USING MULTILAYER PERCEPTRON (MLP) ALGORITHM WITH RESAMPLING." Jurnal Pilar Nusa Mandiri 17, no. 1 (2021): 19–24. http://dx.doi.org/10.33480/pilar.v17i1.2168.

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Telemarketing is a promotion that is considered effective for promoting a product to consumers by telephone, other than that telemarketing is easier to accept because of its direct nature of offering products to consumers. Telemarketing is also considered to help increase a company's revenue. The problem of predicting the success of a bank's telemarketing data must be done using machine learning techniques. Machine learning used in the available historical data is a bank dataset of 45211 instances at 17 features using the multilayer perceptron algorithm (MLP) with resampling. The use of resamp

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Bagui, Sikha S., Dustin Mink, Subhash C. Bagui, and Sakthivel Subramaniam. "Resampling to Classify Rare Attack Tactics in UWF-ZeekData22." Knowledge 4, no. 1 (2024): 96–119. http://dx.doi.org/10.3390/knowledge4010006.

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One of the major problems in classifying network attack tactics is the imbalanced nature of data. Typical network datasets have an extremely high percentage of normal or benign traffic and machine learners are skewed toward classes with more data; hence, attack data remain incorrectly classified. This paper addresses the class imbalance problem using resampling techniques on a newly created dataset, UWF-ZeekData22. This is the first dataset with tactic labels, labeled as per the MITRE ATT&CK framework. This dataset contains about half benign data and half attack tactic data, but specific t

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Nuanmeesri, Sumitra, Wongkot Sriurai, and Nattanon Lamsamut. "Stroke Patients Classification Using Resampling Techniques and Decision Tree Learning." International Journal of Engineering Trends and Technology 69, no. 6 (2021): 115–20. http://dx.doi.org/10.14445/22315381/ijett-v69i6p217.

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Mossman, Douglas. "Resampling Techniques in the Analysis of Non-binormal ROC Data." Medical Decision Making 15, no. 4 (1995): 358–66. http://dx.doi.org/10.1177/0272989x9501500406.

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Ke, Zijun, and Zhiyong Johnny Zhang. "Testing autocorrelation and partial autocorrelation: Asymptotic methods versus resampling techniques." British Journal of Mathematical and Statistical Psychology 71, no. 1 (2017): 96–116. http://dx.doi.org/10.1111/bmsp.12109.

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Elsobky, Alaa, Arabi Keshk, and Mohamed Malhat. "A Comparative Study for Different Resampling Techniques for Imbalanced datasets." IJCI. International Journal of Computers and Information 10, no. 3 (2023): 147–56. http://dx.doi.org/10.21608/ijci.2023.236287.1136.

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Arora, Kashika, Yogita Punjabi, and Ruchi Mathur. "ANALYSIS OF RESAMPLING TECHNIQUES ON CLASSIFICATION MODELS-A CASE STUDY." Journal of Analysis and Computations 17, no. 2 (2023): 287–93. http://dx.doi.org/10.30696/jac.xvii.2.2023.287-293.

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Anis, Maira, Mohsin Ali, Shahid Aslam Mirza, and Malik Mamoon Munir. "Analysis of Resampling Techniques on Predictive Performance of Credit Card Classification." Modern Applied Science 14, no. 7 (2020): 92. http://dx.doi.org/10.5539/mas.v14n7p92.

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Credit card fraud detection has been a very demanding research area due to its huge financial implications and rampant applications in almost every area of life. Credit card fraud datasets are naturally imbalanced by having more legitimate transaction in comparison to the fraudulent transactions.&nbsp; Literature represents numerous studies that are aimed to balance the skewed datasets. There are two major techniques of resampling in balancing these sets i.e. under-sampling and oversampling. However both under-sampling and oversampling techniques suffer from their own set of problems that

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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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