Relevant bibliographies by topics / HYBRID RESAMPLING

Contents

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

Academic literature on the topic 'HYBRID RESAMPLING'

Author: Grafiati
Published: 11 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "HYBRID RESAMPLING"

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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>&nbsp;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.&nbsp;<strong>Method:</strong>&nbsp;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.&nbsp;<
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Dissertations / Theses on the topic "HYBRID RESAMPLING"

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Xu, Yangyi. "Frequentist-Bayesian Hybrid Tests in Semi-parametric and Non-parametric Models with Low/High-Dimensional Covariate." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/71285.

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We provide a Frequentist-Bayesian hybrid test statistic in this dissertation for two testing problems. The first one is to design a test for the significant differences between non-parametric functions and the second one is to design a test allowing any departure of predictors of high dimensional X from constant. The implementation is also given in construction of the proposal test statistics for both problems. For the first testing problem, we consider the statistical difference among massive outcomes or signals to be of interest in many diverse fields including neurophysiology, imaging, e
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Siegmund, Florian. "Dynamic Resampling for Preference-based Evolutionary Multi-objective Optimization of Stochastic Systems : Improving the efficiency of time-constrained optimization." Doctoral thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13088.

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In preference-based Evolutionary Multi-objective Optimization (EMO), the decision maker is looking for a diverse, but locally focused non-dominated front in a preferred area of the objective space, as close as possible to the true Pareto-front. Since solutions found outside the area of interest are considered less important or even irrelevant, the optimization can focus its efforts on the preferred area and find the solutions that the decision maker is looking for more quickly, i.e., with fewer simulation runs. This is particularly important if the available time for optimization is limited, a
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KELLER, AISHWARYA. "HYBRID RESAMPLING AND XGBOOST PREDICTION MODEL USING PATIENT'S INFORMATION AND DRAWING AS FEATURES FOR PARKINSON'S DISEASE DETECTION." Thesis, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19442.

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In the list of most commonly occurring neurodegenerative disorders, Parkinson’s disease ranks second while Alzheimer’s disease tops the list. It has no definite examination for an exact diagnosis. It has been observed that the handwriting of an individual suffering from Parkinson's disease deteriorates considerably. Therefore, many computer vision and micrography-based methods have been used by researchers to explore handwriting as a detection parameter. Yet, these methods suffer from two major drawbacks, i.e., the prediction model's biasedness due to the imbalance in the data an
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Book chapters on the topic "HYBRID RESAMPLING"

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Siegmund, Florian, Amos H. C. Ng, and Kalyanmoy Deb. "Hybrid Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15934-8_25.

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Osei-Brefo, Emmanuel, Richard Mitchell, and Xia Hong. "Hybrid Dual-Resampling and Cost-Sensitive Classification for Credit Risk Prediction." In Artificial Intelligence XL. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47994-6_32.

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Srivastava, Jaya, and Aditi Sharan. "Phishing Website Detection Based on Hybrid Resampling KMeansSMOTENCR and Cost-Sensitive Classification." In Advances in Cognitive Science and Communications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8086-2_69.

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Siegmund, Florian, Amos H. C. Ng, and Kalyanmoy Deb. "Hybrid Dynamic Resampling Algorithms for Evolutionary Multi-objective Optimization of Invariant-Noise Problems." In Applications of Evolutionary Computation. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31153-1_21.

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da Silva, Wellington Betencurte, Julio Cesar Sampaio Dutra, José Mir Justino da Costa, Luiz Alberto da Silva Abreu, Diego Campos Knupp, and Antônio José Silva Neto. "A Hybrid Estimation Scheme Based on the Sequential Importance Resampling Particle Filter and the Particle Swarm Optimization (PSO-SIR)." In Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96433-1_13.

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N. K., Sreeja. "Learning From Class Imbalance." In Handbook of Research on Fireworks Algorithms and Swarm Intelligence. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1659-1.ch005.

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Learning a classifier from imbalanced data is one of the most challenging research problems. Data imbalance occurs when the number of instances belonging to one class is much less than the number of instances belonging to the other class. A standard classifier is biased towards the majority class and therefore misclassifies the minority class instances. Minority class instances may be regarded as rare events or unusual patterns that could potentially have a negative impact on the society. Therefore, detection of such events is considered significant. This chapter proposes a FireWorks-based Hyb
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Veena Rathna Augesteelia, J. "Techniques for Managing Data Imbalance and Detecting Anomalies in IoT Data." In Advanced Machine Learning Models for High Volume Data Processing in IOT Analytics. RADemics Research Institute, 2024. https://doi.org/10.71443/9788197282102-12.

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The proliferation of IOT systems has introduced new challenges in data management, notably concerning data imbalance and anomaly detection. This chapter provides a comprehensive examination of techniques for addressing data imbalance in IoT environments and enhancing anomaly detection capabilities. Data imbalance arises from the disproportionate representation of classes within IoT datasets, leading to skewed model performance and operational inefficiencies. The dynamic nature of IoT data, characterized by temporal and spatial variations, further complicates these challenges. This chapter expl
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Conference papers on the topic "HYBRID RESAMPLING"

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Kranthi, A. Smitha, and D. Haritha Donavalli. "Polycystic Ovary Syndromedetection via Hybrid Model with Improved Resampling Method." In 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS). IEEE, 2024. http://dx.doi.org/10.1109/iciteics61368.2024.10625251.

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Briki, Imane, Maryam Alami Chentoufi, Rachid Ellaia, and Zakaria Charouh. "Improving Road Accident Severity Classification with Cluster-Based Severity Resampling: A Hybrid Approach." In 2024 10th International Conference on Optimization and Applications (ICOA). IEEE, 2024. http://dx.doi.org/10.1109/icoa62581.2024.10754399.

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Pal, Debasmita, Sumitra Mukhopadhyay, and Rajarshi Gupta. "Data Augmentation by Hybrid Data Resampling: Towards Enhanced Performance in Automatic Cardiac Arrhythmia Detection." In 2024 IEEE Calcutta Conference (CALCON). IEEE, 2024. https://doi.org/10.1109/calcon63337.2024.10914252.

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Thar, Kaung Wai, and Thinn Thinn Wai. "A Predictive Analytics Framework for Fraud Detection Using Efficient Resampling Based on Hybrid Ensemble Machine Learning." In 2024 5th International Conference on Advanced Information Technologies (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait65209.2024.10754927.

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Kamal, Hesham, and Maggie Mashaly. "Improving Anomaly Detection in IDS with Hybrid Auto Encoder-SVM and Auto Encoder-LSTM Models Using Resampling Methods." In 2024 6th Novel Intelligent and Leading Emerging Sciences Conference (NILES). IEEE, 2024. http://dx.doi.org/10.1109/niles63360.2024.10753149.

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Shukla, Garima, Vanshaj Awasthi, Prashant Dubey, Saranya, Deepika Shekhawat та Anita Shrotriya. "Hybrid RBC Morphology Analysis and Diagnostic Framework for β- Thalassemia Using SEBlock-CBAM Enhanced MobileNetV2, TabNet with Optuna Optimization and SMOTE-ENN Resampling". У 2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI). IEEE, 2025. https://doi.org/10.1109/iccsai64074.2025.11064027.

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Yuan, Xiaoru, Minh X. Nguyen, Hui Xu, and Baoquan Chen. "Hybrid forward resampling and volume rendering." In the 2003 Eurographics/IEEE TVCG Workshop. ACM Press, 2003. http://dx.doi.org/10.1145/827051.827069.

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Ivaldi, W., M. Milgram, and S. Gentric. "A hybrid resampling framework for facial shape alignment." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.86.

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Halimeh, Mhd Modar, Christian Huemmer, Andreas Brendel, and Walter Kellermann. "Hybrid Particle Filtering Based on an Elitist Resampling Scheme." In 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2018. http://dx.doi.org/10.1109/sam.2018.8448400.

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Cao, Lu, and Yikui Zhai. "Imbalanced Data Classification Based on a Hybrid Resampling SVM Method." In 2015 IEEE 12th Intl. Conf. on Ubiquitous Intelligence and Computing, 2015 IEEE 12th Intl. Conf. on Autonomic and Trusted Computing and 2015 IEEE 15th Intl. Conf. on Scalable Computing and Communications and its Associated Workshops (UIC-ATC-ScalCom). IEEE, 2015. http://dx.doi.org/10.1109/uic-atc-scalcom-cbdcom-iop.2015.275.

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Journal articles
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