Resampling Strategies Research Articles

Machine learning-based study of hardness in polypropylene/carbon nanotube and low-density polyethylene/carbon nanotube composites

In the present work, the hardness prediction of polypropylene/carbon nanotubes (PP/CNT) and low-density polyethylene/carbon nanotubes (LDPE/CNT) composite materials, processed by microwave technique, has been explored using machine learning models i.e. (Random Forest, Support Vector Regression, K-Nearest Neighbors, Linear Regression, and Neural Network). Four input vectors have been used in the construction of proposed network, such as CNT concentration, power, pressure applied, and exposure time. Hardness prediction is one output that has been evolved from the proposed work. This study presents the prediction of hardness based on machine learning models for both PP/CNT and LDPE/CNT composite materials, and the results show that the Random Forest model consistently performs better than the others models in context with performance metrics like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Rate of determination (R2) values. Investigations have been performed on resampling strategies, showing that the jackknife approach enhances model precision and robustness in the case of LDPE/CNT composites. For PP/CNT composite material, it has been noticed that Random Forest gives the highest value of R2 (0.94), whereas Random Forest has the lowest R2 value 0.18 for LDPE/CNT composite material. Random Forest is the most reliable model for predicting the characteristics of PP/CNT composite material due to its ability to handle complex datasets. The LDPE/CNT composite material demonstrates superior prediction accuracy, with a maximum error of just 1.61%, making it a better option for high-precision applications due to enhanced mechanical interactions and improved CNT dispersion.

Synthetic Boosted Resampling Using Deep Generative Adversarial Networks: A Novel Approach to Improve Cancer Prediction from Imbalanced Datasets.

This study examines the effectiveness of different resampling methods and classifier models for handling imbalanced datasets, with a specific focus on critical healthcare applications such as cancer diagnosis and prognosis. To address the class imbalance issue, traditional sampling methods like SMOTE and ADASYN were replaced by Generative Adversarial Networks (GANs), which leverage deep neural network architectures to generate high-quality synthetic data. The study highlights the advantage of GANs in creating realistic, diverse, and homogeneous samples for the minority class, which plays a significant role in mitigating the diagnostic challenges posed by imbalanced data. Four types of classifiers, Boosting, Bagging, Linear, and Non-linear, were assessed to evaluate their performance using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Baseline performance without resampling showed significant limitations, underscoring the need for resampling strategies. Using GAN-generated data notably improved the detection of minority instances and overall classification performance. The average ROC AUC value increased from baseline levels of approximately 0.8276 to over 0.9734, underscoring the effectiveness of GAN-based resampling in enhancing model performance and ensuring more balanced detection across classes. With GAN-based resampling, GradientBoosting classifier achieved a ROC AUC of 0.9890, the highest among all models, demonstrating the effectiveness of GAN-generated data in enhancing performance. The findings underscore that advanced models like Boosting and Bagging, when paired with effective resampling strategies such as GANs, are better suited for handling imbalanced datasets and improving predictive accuracy in healthcare applications.

Identifying artificial intelligence–generated content in online Q&A communities through interpretable machine learning

This study aims to construct a comprehensive feature system for identifying artificial intelligence–generated content (AIGC) in online Q&A communities, thus uncovering the key factors and mechanisms influencing the identification of AIGC. First, based on the theory of systemic functional linguistics (SFL) and information quality (IQ), this article extracts vocabulary, content, structure, and emotional features from the text, and identifies the AIGC through nine mainstream machine learning algorithms. Subsequently, three widely used resampling strategies are exploited to address the category imbalance problem. The grid search optimisation algorithm fine-tunes different combinations of parameters to improve the performance of the identification classifier. Finally, SHAP values are introduced to evaluate and elucidate the global feature importance and feature influence mechanism. A Chinese corpus from the Zhihu Q&A community is constructed to verify the validity of these methods. The experimental results show that the eXtreme Gradient Boosting (XGBoost) model optimised with hybrid sampling and grid search parameters exhibits excellent performance in identifying AI-generated text, which achieves an F1-score of 0.9935, an improvement of 0.11 percentage points over the original model. In addition, all four dimensions of features constructed in this article contribute to AI-generated text identification, and the results of feature interpretability analysis show the greatest impact of features that focus on content readability. The study facilitates the identification and labelling of AIGC in online Q&A communities, thereby enhancing transparency and accountability of information shared online.

MinoritySalMix and adaptive semantic weight compensation for long-tailed classification

MinoritySalMix and adaptive semantic weight compensation for long-tailed classification

A human-in-the-loop ensemble fusion framework for road crash prediction: coping with imbalanced heterogeneous data from the driver-vehicle-environment system

ABSTRACT Road accidents are an inevitable aspect of daily life, and predicting crashes is crucial for minimizing disruptions and advancing intelligent transportation technologies. This study aims to design an ensemble fusion decision system using various base classifiers and a meta-classifier to improve crash prediction efficiency within the driver-vehicle-environment system. We adopted a data-driven strategy to analyze four categories of features—driver demographics, vehicle telemetry, driver inputs, and environmental conditions—collected from a driving simulator. Optimized modeling strategies using AdaBoost, XGBoost, GBM, LightGBM, and CatBoost were implemented. Moreover, statistical logit models were also used to assess the likelihood of crashes and the correlations among key variables. Furthermore, three resampling strategies, SMOTE-TL, SMOTE-ENN, and ADASYN, were employed to address class imbalance. The best performance was achieved with GBM, XGBoost, and AdaBoost as base classifiers, SMOTE-TL for balancing, and CatBoost as the meta-classifier, with 89.78% precision, 95.69% recall, and 92.64% F1-score.

PVF-10: A high-resolution unmanned aerial vehicle thermal infrared image dataset for fine-grained photovoltaic fault classification

PVF-10: A high-resolution unmanned aerial vehicle thermal infrared image dataset for fine-grained photovoltaic fault classification

Appraising protein conformational changes by resampling time-resolved serial x-ray crystallography data.

With the development of serial crystallography at both x-ray free electron laser and synchrotron radiation sources, time-resolved x-ray crystallography is increasingly being applied to study conformational changes in macromolecules. A successful time-resolved serial crystallography study requires the growth of microcrystals, a mechanism for synchronized and homogeneous excitation of the reaction of interest within microcrystals, and tools for structural interpretation. Here, we utilize time-resolved serial femtosecond crystallography data collected from microcrystals of bacteriorhodopsin to compare results from partial occupancy structural refinement and refinement against extrapolated data. We illustrate the domain wherein the amplitude of refined conformational changes is inversely proportional to the activated state occupancy. We illustrate how resampling strategies allow coordinate uncertainty to be estimated and demonstrate that these two approaches to structural refinement agree within coordinate errors. We illustrate how singular value decomposition of a set of difference Fourier electron density maps calculated from resampled data can minimize phase bias in these maps, and we quantify residual densities for transient water molecules by analyzing difference Fourier and Polder omit maps from resampled data. We suggest that these tools may assist others in judging the confidence with which observed electron density differences may be interpreted as functionally important conformational changes.

A novel generative adversarial network for improving crash severity modeling with imbalanced data

A novel generative adversarial network for improving crash severity modeling with imbalanced data

The effects of pension on depressive symptoms in Chinese older adults: A moderated multiple mediator model.

This study investigates the impact of pension on depressive symptoms among Chinese older adults. Additional effort is made to test the mediating effect of multidimensional downward intergenerational support and the moderating effect of age on this relationship. A total of 1828 Chinese older community-dwellers who met our inclusion criteria are drawn from the 2018 China Health and Retirement Longitudinal Study. Multivariate regression modeling is applied to analyze the effect of pensions on depressive symptoms of older adults. Additionally, bootstrap method with resampling strategies is used to estimate the mediating effect of three dimensions of downward intergenerational support (instrumental, emotional, and financial support). Further, Johnson-Neyman technique is employed to analysis and visualize the moderating effect of age. The findings reveal a significant inverse relationship between pension levels and depressive symptoms (B=-6.664, SE=2.826, p<0.05). The analysis shows that downward intergenerational emotional support (B=-0.195, Boot SE=0.103, 95% Boot CI [-0.404, -0.003]) serves as a partial mediator in this relationship. Furthermore, the results highlight the moderating role of age in the linkage between pension and depressive symptoms (B=0.065, SE=0.039, p<0.1). This investigation is pioneering in simultaneously assessing the mediating role of multidimensional downward intergenerational support and the moderating effect of age in the context of pension and depressive symptoms. The study underscores the necessity of an interdisciplinary approach in devising comprehensive intervention strategies. These should encompass pension policy consultation, respite services, and other crucial elements aimed at mitigating the severity or reducing the risk of depressive symptoms among the older adults.

Impact of Nature of Medical Data on Machine and Deep Learning for Imbalanced Datasets: Clinical Validity of SMOTE Is Questionable

Dataset imbalances pose a significant challenge to predictive modeling in both medical and financial domains, where conventional strategies, including resampling and algorithmic modifications, often fail to adequately address minority class underrepresentation. This study theoretically and practically investigates how the inherent nature of medical data affects the classification of minority classes. It employs ten machine and deep learning classifiers, ranging from ensemble learners to cost-sensitive algorithms, across comparably sized medical and financial datasets. Despite these efforts, none of the classifiers achieved effective classification of the minority class in the medical dataset, with sensitivity below 5.0% and area under the curve (AUC) below 57.0%. In contrast, the similar classifiers applied to the financial dataset demonstrated strong discriminative power, with overall accuracy exceeding 95.0%, sensitivity over 73.0%, and AUC above 96.0%. This disparity underscores the unpredictable variability inherent in the nature of medical data, as exemplified by the dispersed and homogeneous distribution of the minority class among other classes in principal component analysis (PCA) graphs. The application of the synthetic minority oversampling technique (SMOTE) introduced 62 synthetic patients based on merely 20 original cases, casting doubt on its clinical validity and the representation of real-world patient variability. Furthermore, post-SMOTE feature importance analysis, utilizing SHapley Additive exPlanations (SHAP) and tree-based methods, contradicted established cerebral stroke parameters, further questioning the clinical coherence of synthetic dataset augmentation. These findings call into question the clinical validity of the SMOTE technique and underscore the urgent need for advanced modeling techniques and algorithmic innovations for predicting minority-class outcomes in medical datasets without depending on resampling strategies. This approach underscores the importance of developing methods that are not only theoretically robust but also clinically relevant and applicable to real-world clinical scenarios. Consequently, this study underscores the importance of future research efforts to bridge the gap between theoretical advancements and the practical, clinical applications of models like SMOTE in healthcare.

Analysing the Relationship between Spatial Resolution, Sharpness and Signal-to-Noise Ratio of Very High Resolution Satellite Imagery Using an Automatic Edge Method

Assessing the performance of optical imaging systems is crucial to evaluate their capability to satisfy the product requirements for an Earth Observation (EO) mission. In particular, the evaluation of image quality is undoubtedly one of the most important, critical and problematic aspects of remote sensing. It involves not only pre-flight analyses, but also continuous monitoring throughout the operational lifetime of the observing system. The Ground Sampling Distance (GSD) of the imaging system is often the only parameter used to quantify its spatial resolution, i.e., its capability to resolve objects on the ground. In practice, this feature is also heavily influenced by other image quality parameters such as the image sharpness and Signal-to-Noise Ratio (SNR). However, these last two aspects are often analysed separately, using unrelated methodologies, complicating the image quality assessment and posing standardisation issues. To this end, we expanded the features of our Automatic Edge Method (AEM), which was originally developed to simplify and automate the estimate of sharpness metrics, to also extract the image SNR. In this paper we applied the AEM to a wide range of optical satellite images characterised by different GSD and Pixel Size (PS) with the objective to explore the nature of the relationship between the components of overall image quality (image sharpness, SNR) and product geometric resampling (expressed in terms of GSD/PS ratio). Our main objective is to quantify how the sharpness and the radiometric quality of an image product are affected by different product geometric resampling strategies, i.e., by distributing imagery with a PS larger or smaller than the GSD of the imaging system. The AEM allowed us to explore this relationship by relying on a vast amount of data points, which provide a robust statistical significance to the results expressed in terms of sharpness metrics and SNR means. The results indicate the existence of a direct relationship between the product geometric resampling and the overall image quality, and also highlight a good degree of correlation between the image sharpness and SNR.

Correlation Between Life Satisfaction and Symptoms of ADHD in Dental Students: The Mediation of Resilience.

This study explores the correlation between life satisfaction and ADHD symptoms. It also discusses whether resilience mediates the correlation between ADHD symptoms and life satisfaction. We surveyed 297 dental students. A total of 291 completed a self-report scale consisting of the Adult ADHD Self-Report Scale, Wender Utah Rating Scale, Life Satisfaction Scale, and Conner-Davidson Resilience Scale. The study used hierarchical linear regression analysis, resampling, and asymptotic strategies for data processing. The ADHD screening results of the self-report scale were positive for 6.87% of the students. This positive rate differed among participants of diverse ages and varying paternal education levels. ADHD symptoms were negatively correlated with life satisfaction and resilience. Life satisfaction was observably positively associated with resilience. Resilience serves as a mediating role between life satisfaction and the two symptoms of ADHD. Resilience intervention programs can enhance the life satisfaction of dental students, especially those with positive ADHD symptoms screening.

Using data resampling and category weight adjustment to solve sample imbalance

The purpose of this thesis is to investigate the application of artificial intelligence and machine learning in solving the sample imbalance problem. The sample imbalance problem refers to the phenomenon that the number of different categories of samples in the training data varies greatly, resulting in the poor performance of traditional machine learning algorithms on a few categories of samples. To address this problem, this paper proposes a new approach combining data resampling and category weight adjustment strategies. First, the sample distribution of the dataset is adjusted by undersampling and oversampling techniques to balance the number of samples from different categories. Then, during the model training process, different weights are assigned to the samples of different categories so that the model pays more attention to the samples of a few categories. The experimental results show that the method achieves significant performance improvement on multiple datasets. In addition, this paper compares other commonly used methods for solving the sample imbalance problem and analyzes and discusses them in detail. Finally, this study offers a practical solution to the problem of sample imbalance and provides guidance for research in related fields.

Customer churn prediction in imbalanced datasets with resampling methods: A comparative study

Customer churn prediction in imbalanced datasets with resampling methods: A comparative study

Hierarchical Knowledge Guided Learning for Real-World Retinal Disease Recognition.

In the real world, medical datasets often exhibit a long-tailed data distribution (i.e., a few classes occupy the majority of the data, while most classes have only a limited number of samples), which results in a challenging long-tailed learning scenario. Some recently published datasets in ophthalmology AI consist of more than 40 kinds of retinal diseases with complex abnormalities and variable morbidity. Nevertheless, more than 30 conditions are rarely seen in global patient cohorts. From a modeling perspective, most deep learning models trained on these datasets may lack the ability to generalize to rare diseases where only a few available samples are presented for training. In addition, there may be more than one disease for the presence of the retina, resulting in a challenging label co-occurrence scenario, also known as multi-label, which can cause problems when some re-sampling strategies are applied during training. To address the above two major challenges, this paper presents a novel method that enables the deep neural network to learn from a long-tailed fundus database for various retinal disease recognition. Firstly, we exploit the prior knowledge in ophthalmology to improve the feature representation using a hierarchy-aware pre-training. Secondly, we adopt an instance-wise class-balanced sampling strategy to address the label co-occurrence issue under the long-tailed medical dataset scenario. Thirdly, we introduce a novel hybrid knowledge distillation to train a less biased representation and classifier. We conducted extensive experiments on four databases, including two public datasets and two in-house databases with more than one million fundus images. The experimental results demonstrate the superiority of our proposed methods with recognition accuracy outperforming the state-of-the-art competitors, especially for these rare diseases.

Resampling Strategy in Sequential Monte Carlo for Constrained Sampling Problems

Sequential Monte Carlo (SMC) methods are a class of Monte Carlo methods that are used to obtain random samples of a high dimensional random variable in a sequential fashion. Many problems encountered in applications often involve different types of constraints. These constraints can make the problem much more challenging. In this paper, we formulate a general framework of using SMC for constrained sampling problems based on forward and backward pilot resampling strategies. We review some existing methods under the framework and develop several new algorithms. It is noted that all information observed or imposed on the underlying system can be viewed as constraints. Hence the approach outlined in this paper can be useful in many applications.

A prospective association between social isolation and cognitive performance among older adults in Europe: the role of loneliness and poor oral health

Objectives The relationship between social isolation, loneliness, and tooth loss and cognition in older people is poorly understood. We examine how social isolation and cognitive performance are associated prospectively among older adults, as well as how tooth loss and loneliness are related to this association. Methods Using data from 26,168 participants aged ≥50 from the Survey of Health, Ageing and Retirement in Europe (SHARE), we explored the association between social isolation, loneliness, tooth loss and cognition. We used bootstrapping with resampling strategies for testing a moderated mediating model. Results Higher social isolation was associated with poorer cognitive performance (B = −0.20, 95% CI = −0.03, −0.01; R 2 =0.60), an association mediated by the respondent’s number of missing teeth (B = −0.001, 95% CI = −0.002, −0.001). Higher levels of social isolation were associated with a greater number of missing teeth, and a higher number of missing teeth was linked with poorer cognition. We also found that loneliness moderated the relationship between social isolation and both the number of missing teeth (B = −0.11, p = 0.047) and cognitive performance. Conclusion In later life, social isolation and loneliness are associated with shoddy oral health and poor cognitive status. Clinicians and policymakers should be aware of both the association between social isolation and feelings of loneliness on dentition and oral health and their relationship to the cognitive status of older adults.

Resampling and Ensemble Strategies for Churn Prediction

Churn analysis is a customer relationship management analytics that companies implement to predict the customers who are likely to terminate doing business with them. The success of marketing efforts to retain the existing customers is possible only if probable churners are correctly specified beforehand. Therefore, having powerful models with high prediction capabilities that lead to a profit growth is crucial. The imbalanced nature of churn datasets negatively effects the classification performance of machine learning methods. This study examines resampling –over- and under-sampling- and ensemble learning –bagging, boosting, and stacking– strategies integrated with the cross-validation procedure on imbalanced churn prediction. The experimental results, which are compared to the results of Support Vector Machines taken as the benchmark, show that ensemble methods improve the prediction performances. Also, applying over-sampling achieves a noticeable performance in comparison with the under-sampling approach.

Efficient Fusion Decision System for Predicting Road Crash Events: A Comparative Simulator Study for Imbalance Class Handling

Road crash events are a fact of life. Although significant progress have been made in adopting machine learning techniques for analyzing road crashes, there has been limited emphasis on evaluating crash events within data fusion systems. The primary purpose of this study is to outline and validate a comparative safety analysis of an ensemble fusion system founded on the use of different base classifiers and a meta-classifier to procure more efficient crash prediction. Three categories of features namely vehicle-telemetry, driver-inputs and environmental-conditions have been collected using a driving-simulator in order to identify the crash strongest precursors through feature extraction technique. Furthermore, optimized strategies using AdaBoost, XGBoost, RF, GBM, LightGBM, CatBoost and KNN techniques were implemented to establish effective predictions within a fusion-based system. To ensure that the proposed system provide superior decisions given the infrequent nature of crash events, an imbalance-learning approach was conducted based on three resampling strategies: over-sampling, under-sampling and SMOTE-Tomek-Links. The findings depict that the superior performance has been attained when adopting LightGBM, CatBoost and KNN as base classifiers along with SMOTE-TL as balancing technique and XGBoost as meta-classifier with 89.19% precision, 96.77% recall and 92.83% f1-score. To our knowledge, there has been a limited interest, if not at all, at endorsing a fusion-based system examining the impact of real-time features' combinations on the prediction of road crashes while providing a critical analysis of class-imbalance. Overall, the findings emphasized the relevance of the explanatory features and can be endorsed in designing efficient intelligent transportation systems.

Conditional Particle Filters with Bridge Backward Sampling

Conditional particle filters (CPFs) with backward/ancestor sampling are powerful methods for sampling from the posterior distribution of the latent states of a dynamic model such as a hidden Markov model. However, the performance of these methods deteriorates with models involving weakly informative observations and/or slowly mixing dynamics. Both of these complications arise when sampling finely time-discretized continuous-time path integral models, but can occur with hidden Markov models too. Multinomial resampling, which is commonly employed with CPFs, resamples excessively for weakly informative observations and thereby introduces extra variance. Furthermore, slowly mixing dynamics render the backward/ancestor sampling steps ineffective, leading to degeneracy issues. We detail two conditional resampling strategies suitable for the weakly informative regime: the so-called “killing” resampling and the systematic resampling with mean partial order. To avoid the degeneracy issues, we introduce a generalization of the CPF with backward sampling that involves auxiliary “bridging” CPF steps that are parameterized by a blocking sequence. We present practical tuning strategies for choosing an appropriate blocking. Our experiments demonstrate that the CPF with a suitable resampling and the developed “bridge backward sampling” can lead to substantial efficiency gains in the weakly informative and slow mixing regime. Supplementary materials for this article are available online.