Relevant bibliographies by topics / Resampling Techniques

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Resampling Techniques'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Resampling Techniques"

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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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Dissertations / Theses on the topic "Resampling Techniques"

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Higgs, Trent. "Protein Structure Prediction using Feature-Based Resampling Techniques." Thesis, Griffith University, 2013. http://hdl.handle.net/10072/365543.

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A protein is formed by a string of amino acids folding into a specic three-dimensional shape. Experimental approaches used to determine a protein's three-dimensional structure are time consuming and resource demanding. Therefore, computational methods that predict protein structure have been introduced. Computational protein structure predition (PSP) methods can be grouped into three main categories: comparative modelling, threading, and ab initio. Ab initio methods try to predict a protein's three-dimensional structure from its sequence alone. This is based on `Ansen's Thermodynamic Hypothesi
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Shah, Kashif. "Model adaptation techniques in machine translation." Phd thesis, Université du Maine, 2012. http://tel.archives-ouvertes.fr/tel-00718226.

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Nowadays several indicators suggest that the statistical approach to machinetranslation is the most promising. It allows fast development of systems for anylanguage pair provided that sufficient training data is available.Statistical Machine Translation (SMT) systems use parallel texts ‐ also called bitexts ‐ astraining material for creation of the translation model and monolingual corpora fortarget language modeling.The performance of an SMT system heavily depends upon the quality and quantity ofavailable data. In order to train the translation model, the parallel texts is collected fromvario
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SHI, Juanjuan. "Morphology-based Fault Feature Extraction and Resampling-free Fault Identification Techniques for Rolling Element Bearing Condition Monitoring." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33422.

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As the failure of a bearing could cause cascading breakdowns of the mechanical system and then lead to costly repairs and production delays, bearing condition monitoring has received much attention for decades. One of the primary methods for this purpose is based on the analysis of vibration signal measured by accelerometers because such data are information-rich. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault-generated impulses, interferences from other machine components, and background noise, where fault-induce
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Aslan, Serdar. "Nonlinear Estimation Techniques Applied To Econometric." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605649/index.pdf.

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This thesis considers the filtering and prediction problems of nonlinear noisy econometric systems. As a filter/predictor, the standard tool Extended Kalman Filter and new approaches Discrete Quantization Filter and Sequential Importance Resampling Filter are used. The algorithms are compared by using Monte Carlo Simulation technique. The advantages of the new algorithms over Extended Kalman Filter are shown.
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Jagelid, Michelle, and Maria Movin. "A Comparison of Resampling Techniques to Handle the Class Imbalance Problem in Machine Learning : Conversion prediction of Spotify Users - A Case Study." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208876.

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Spotify uses a freemium business model, meaning that it has two main products, one free limited and one premium for paying customers. In this study we investigated machine learning models’ abilities, given user activity data, to predict conversion from free to premium. Predicting which of the users convert from free to premium was a class-imbalanced problem, meaning that the ratio of converters and non-converters was skewed. Three methods were investigated: logistic regression, decision trees, and gradient boosting trees. We also studied if different resampling methods, which balance the train
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Rousselet, Gustave. "Classification of post-wildfire aerial imagery using convolutional neural networks : A study of machine learning and resampling techniques to assist post-wildfire efforts." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259695.

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Assessment of post-wildfire damages to human structures is a manual task which currently uses ground-level observations of the structures by a human inspector to classify the burn severity of the affected structure. This study investigated the potential of using machine learning and specifically computer vision techniques in order to produce a classification of burn severity using post-wildfire aerial imagery. Specifically, a convolutional neural network model was trained on post-wildfire aerial imagery of affected human structures, and learned to classify their burn severity. The dataset used
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Ramanayaka, Mudiyanselage Asanga. "Data Engineering and Failure Prediction for Hard Drive S.M.A.R.T. Data." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1594957948648404.

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Tsung-YuTu and 涂宗裕. "Classification of Data by Resampling Techniques." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/90943311151044595183.

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Ou-Yung, Raoul, and 歐陽若. "Tone Modulation By Using Pitch-Synchronous Nonlinearly Resampling Techniques." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/49076571457433882382.

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碩士<br>國立中興大學<br>應用數學系<br>84<br>In this thesis, we developed a new method to modulate F0. The new method exploits nonlinear resampling techniques and the actual changes in F0 modulationwe observed to accomplish such a task. We use a 3rd order modified Bezier curve as the resampling path, which is obtained from the actual changes in F0 modulation, A DTW (Dynamic Time Warping)algorithm is used to figure out the mapping relations between different tones. Then we use the modified Bezier curv
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Deus, Rui Pedro Gonçalves de. "Modelos de avistamento de contactos no mar: estimação com distâncias de detecção inicial." Master's thesis, 2010. http://hdl.handle.net/10362/2722.

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Dissertação apresentada como requisito parcial para a obtenção do grau de Mestre em Estatística e Gestão da Informação<br>Na Teoria de Busca, a capacidade de detecção de um sensor é representada por uma probabilidade de detecção condicional à distância entre o alvo e o sensor. Na busca e detecção de alvos no mar, com sistemas de detecção passivos (visuais) ou activos (radar), é natural que o sensor e o alvo se encontrem em movimento. A detecção é possível quando o movimento relativo entre o sensor e o alvo os aproxima o suficiente para que a detecção ocorra. A função que representa a prob
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Books on the topic "Resampling Techniques"

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McMurry, Timothy, and Dimitris Politis. Resampling methods for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.7.

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This article examines the current state of methodological and practical developments for resampling inference techniques in functional data analysis, paying special attention to situations where either the data and/or the parameters being estimated take values in a space of functions. It first provides the basic background and notation before discussing bootstrap results from nonparametric smoothing, taking into account confidence bands in density estimation as well as confidence bands in nonparametric regression and autoregression. It then considers the major results in subsampling and what i
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Haig, Brian D. Exploratory Data Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190222055.003.0002.

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Chapter 2 is concerned with modern data analysis. It focuses primarily on the nature, role, and importance of exploratory data analysis, although it gives some attention to computer-intensive resampling methods. Exploratory data analysis is a process in which data are examined to reveal potential patterns of interest. However, the use of traditional confirmatory methods in data analysis remains the dominant practice. Different perspectives on data analysis, as they are shaped by four different accounts of scientific method, are provided. A brief discussion of John Tukey’s philosophy of teachin
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Book chapters on the topic "Resampling Techniques"

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Kolassa, John E. "Resampling Techniques." In An Introduction to Nonparametric Statistics. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429202759-10.

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Latpate, Raosaheb, Jayant Kshirsagar, Vinod Kumar Gupta, and Girish Chandra. "Resampling Techniques." In Advanced Sampling Methods. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0622-9_9.

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Masters, Timothy. "Miscellaneous Resampling Techniques." In Assessing and Improving Prediction and Classification. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3336-8_5.

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Ramasubramanian, Karthik, and Abhishek Singh. "Sampling and Resampling Techniques." In Machine Learning Using R. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2334-5_3.

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Ramasubramanian, Karthik, and Abhishek Singh. "Sampling and Resampling Techniques." In Machine Learning Using R. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4215-5_3.

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Salsburg, David S. "Permutation Tests and Resampling Techniques." In The Use of Restricted Significance Tests in Clinical Trials. Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-4414-1_9.

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Wendler, Tilo, and Sören Gröttrup. "Imbalanced Data and Resampling Techniques." In Data Mining with SPSS Modeler. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54338-9_10.

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Qureshi, Taimur, and Djamel A. Zighed. "Using Resampling Techniques for Better Quality Discretization." In Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03070-3_6.

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Pigeot, Iris. "Special Resampling Techniques in Categorical Data Analysis." In Contributions to Statistics. Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-57991-2_10.

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Letteri, Ivan, Antonio Di Cecco, Abeer Dyoub, and Giuseppe Della Penna. "Imbalanced Dataset Optimization with New Resampling Techniques." In Lecture Notes in Networks and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82196-8_15.

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Conference papers on the topic "Resampling Techniques"

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Saharuna, Zawiyah, and Tohari Ahmad. "Multiclass Imbalance Resampling Techniques for Network Intrusion Detection." In 2024 10th International Conference on Smart Computing and Communication (ICSCC). IEEE, 2024. http://dx.doi.org/10.1109/icscc62041.2024.10690582.

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Alhyasat, Ghayda, and Bushra Alhijawi. "Experimental Evaluation of Resampling Techniques for Imbalanced Stroke Data." In 2025 International Conference on New Trends in Computing Sciences (ICTCS). IEEE, 2025. https://doi.org/10.1109/ictcs65341.2025.10989344.

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Tojeiro, Carlos A. C., Thiago J. Lucas, Leandro A. Passos, et al. "Enhancing Cyberattack Detection in IoT Environments Through Advanced Resampling Techniques." In 2024 31st International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2024. http://dx.doi.org/10.1109/iwssip62407.2024.10634015.

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Ma, Xiang, Yongshun Wu, Haoliang Yu, Donghui Wang, Sujun Yang, and Song Shen. "Development and Validation of Integral Algorithms Based on Data Resampling Techniques." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743837.

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Kurniawan, Juan Marchell, Ida Bagus Kuwera Girindra Putra, Stanley Yong, and Mediana Aryuni. "Handling Imbalanced Dataset in Online Learning Performance Prediction Using Resampling Techniques." In 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2024. https://doi.org/10.1109/isriti64779.2024.10963560.

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Nandeesh, Talari, and Ashu Mehta. "Improving Classifier Performance on Imbalanced Software Defect Datasets Using Resampling Techniques." In 2025 International Conference on Frontier Technologies and Solutions (ICFTS). IEEE, 2025. https://doi.org/10.1109/icfts62006.2025.11031775.

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Puvanendran, Rukshani, Pirunthavi Wijikumar, Tharuni Rathnayaka, et al. "Comparative Analysis of Resampling Techniques on Class Imbalance in Body Shaming Phrase Detection." In 2024 Moratuwa Engineering Research Conference (MERCon). IEEE, 2024. http://dx.doi.org/10.1109/mercon63886.2024.10688774.

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Asif, Muhammad, Jawad Ali Bhatti, Muhammad Safwan, Muhammad Rafay Khan, Fahad Ahmed Siddiqui, and Muhammad Rehan. "A Comparative Analysis of Resampling Techniques for Addressing Class Imbalance in Multiclass Classification." In 2024 Global Conference on Wireless and Optical Technologies (GCWOT). IEEE, 2024. https://doi.org/10.1109/gcwot63882.2024.10805619.

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Thotakura, Yaswanth Chowdary, Dinesh Manikanta Yarramsetty, Kalyan Kumar Doppalapudi, Sai Shasank Alaparthi, and Sumalatha Saleti. "Enhancing Customer Churn Prediction: Advanced Models and Resampling Techniques in Dynamic Business Environments." In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC). IEEE, 2024. https://doi.org/10.1109/icec59683.2024.10837309.

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Diwandari, Saucha, Adhistya Erna Permanasari, and Indriana Hidayah. "Comparative Evaluation of Resampling Techniques on Machine Learning for Predicting Customer Purchasing Behavior." In 2024 8th International Conference on Information Technology (InCIT). IEEE, 2024. https://doi.org/10.1109/incit63192.2024.10810628.

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Reports on the topic "Resampling Techniques"

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Kraut, Shawn, and Jeffrey Krolik. Moving Target Depth Estimation for Passive Sonar, Using Sequential Resampling Techniques. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada405125.

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Doss, Hani. Statistical Inference for Coherent Systems from Partial Information and in Resampling Techniques in Survival Analysis. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada277382.

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