Anomaly Detection in Medical Images Using SMOTE Algorithm: A Comprehensive Approach

In the semiconductor manufacturing industry, Automatic Defect Classification (ADC) plays an important role in maintaining high wafer inspection quality and reducing yield loss. ADC performance has benefitted from using machine learning (ML) algorithms; however, performance is negatively affected by the data imbalance and limited amounts of training data. Synthetic Minority Oversampling Technique (SMOTE) is an oversampling technique to adjust the skewed class distribution of a dataset so that the bias of the majority class is reduced. This paper shows that applying SMOTE achieved higher accuracy and purity on two imbalanced datasets, consisting of scanning electron microscopy (SEM) images collected with ASML-HMI eP™ and eScan® series inspection tools. The ML models are also less sensitive to the selection of hyperparameters when SMOTE is applied. We also show that better classification results can be obtained with less training samples with SMOTE; we conducted an experiment where a ML model trained on only 25% of samples with SMOTE achieved a higher ADC accuracy and purity performance compared to the same ML model trained on all samples but without SMOTE. In another experiment using a highly imbalanced SEM dataset with very few counts of the defect-of- interest (DOI), the combination of SMOTE and random undersampling of the majority class improves the accuracy by up to 5x while maintaining the same level of purity.

AimTo improve the accuracy of inferences on habitat associations and distribution patterns of rare species by combining machine‐learning, spatial filtering and resampling to address class imbalance and spatial bias of large volumes of citizen science data.InnovationModelling rare species’ distributions is a pressing challenge for conservation and applied research. Often, a large number of surveys are required before enough detections occur to model distributions of rare species accurately, resulting in a data set with a high proportion of non‐detections (i.e. class imbalance). Citizen science data can provide a cost‐effective source of surveys but likely suffer from class imbalance. Citizen science data also suffer from spatial bias, likely from preferential sampling. To correct for class imbalance and spatial bias, we used spatial filtering to under‐sample the majority class (non‐detection) while maintaining all of the limited information from the minority class (detection). We investigated the use of spatial under‐sampling with randomForest models and compared it to common approaches used for imbalanced data, the synthetic minority oversampling technique (SMOTE), weighted random forest and balanced random forest models. Model accuracy was assessed using kappa, Brier score and AUC. We demonstrate the method by evaluating habitat associations and seasonal distribution patterns using citizen science data for a rare species, the tricoloured blackbird (Agelaius tricolor).Main ConclusionsSpatial under‐sampling increased the accuracy of each model and outperformed the approach typically used to direct under‐sampling in the SMOTE algorithm. Our approach is the first to characterize winter distribution and movement of tricoloured blackbirds. Our results show that tricoloured blackbirds are positively associated with grassland, pasture and wetland habitats, and negatively associated with high elevations or evergreen forests during both winter and breeding seasons. The seasonal differences in distribution indicate that individuals move to the coast during the winter, as suggested by historical accounts.

The rise of IoT technologies has increased the need for robust threat detection to address growing cyber-physical risks. Traditional machine learning (ML) models often struggle with imbalanced datasets, particularly in detecting rare threats. This study tackles this challenge using the "IoT_Modbus" dataset, a benchmark for multiclass cybersecurity classification, and explores advanced resampling techniques to enhance model performance. By combining methods like Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) with ensemble classifiers, Random Forest (RF), Gradient Boosting, XGBoost, LightGBM, and CatBoost, significant improvements in accuracy were achieved. RF outperformed others, reaching 99.06% accuracy with SMOTE and 99.00% with ADASYN. Among boosting-based models, XGBoost led with 86.21% (SMOTE) and 86.63% (ADASYN), while CatBoost followed closely. These results highlight the effectiveness of resampling in addressing class imbalance and improving predictive performance. Visual tools like heatmaps and accuracy charts further clarify these trends. This research provides cybersecurity professionals with a practical framework to enhance IoT threat detection and underscores the importance of resampling strategies for better classification outcomes. By guiding model selection for imbalanced datasets, it offers valuable insights for securing IoT systems in an interconnected world.

Lithology identification during drilling plays a vital role in geological and geotechnical exploration, as it facilitates the early detection of formation-related hazards and supports the development of optimized mining strategies. Traditional lithology identification research involves problems such as fuzzy indicator characteristics and unbalanced sample quantities, which affect the accuracy and interpretability of model identification. In order to solve these problems, the Shanxi Guoqiang Coal Mine was taken as the research object, and a combined machine learning model was used to conduct a study on lithology identification during drilling. First, the least absolute shrinkage and selection operator (LASSO) algorithm was used to screen the independent variables and retain the parameters that contributed the most to lithology identification. Then, the synthetic minority oversampling technique (SMOTE) algorithm was used to expand the data samples, increase the amounts of minority sample data, and keep the ratios of various lithology data at 1:1. Then, the Bayesian optimization (BO) algorithm was used to optimize the penalty factor C and kernel function hyperparameter γ—two important parameters of the support vector machine (SVM) model—and the BO-SVM lithology identification model was established. Finally, the data samples were processed, and the results were compared with those of single models and unbalanced sample processing to evaluate their effect. The results showed the following: during the drilling process, the four indicators of drilling speed, mud pressure, slurry flow rate, and torque are strongly correlated with the lithology and can be used for lithology identification and classification research. After the data set was oversampled using the SMOTE algorithm, each model had better robustness and generalization ability; the classification result evaluation indicators were also greatly improved, especially for the random forest model, which had a poor original evaluation effect. The BO algorithm was used to optimize the parameters of the SVM model and establish a combined model that correctly identified 95 groups of data out of 96 groups of test samples with an identification accuracy rate of 99%, which was better than that of the traditional machine learning model. The evaluation results were compared with measured data, which confirmed the reliability of the combined model classification method and its potential to be extended to lithology identification and classification work.

While the rapid development of vaccines during the COVID-19 pandemic helped save billions of lives, a significant percentage of the population reported adverse reactions after vaccination. Post-vaccination surveys were conducted to understand the possible side effects. Analyzing and understanding the side effects enables the relevant stakeholders to gain more understanding of the vaccine and allows the individual to make informed decisions about whether to receive the vaccine. This work aims to identify a robust approach that can be used to predict the possibility of potential adverse symptoms. A dataset comprising 840 participants with 15 Post-Vaccination Symptoms was considered for the study. Synthetic Minority Oversampling TEchnique (SMOTE) was used to handle the class imbalance of the dataset. A combination of SMOTE and ensemble machine learning models was used to predict the adverse reactions to COVID-19 vaccines. The ensemble Machine Learning (ML) models that are considered for this study are Random Forest, Extreme Gradient Boosting Machine (XGBoost), Light Gradient Boosting Machine, and AdaBoost. The metrics accuracy, precision, recall and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) were used to measure the performance of the models. The dataset was pre-processed to handle missing values and one-hot encoding was applied to convert categorical variables into the numerical format. Insights into the data distribution and relationships between variables were gained through exploratory data and correlation analysis, respectively. Class imbalance in the target variables was addressed using SMOTE, resulting in a significantly improved F1-score and ROC-AUC score. Among the ensemble ML models, XGBoost delivered the best performance metrics. A combined performance score was calculated by averaging the F1-score and accuracy to identify the best model. XGBoost obtained the highest performance score among the ensemble ML models, and its performance is further enhanced by performing the threshold adjustment using the maximum F1- score strategy. The findings suggest that the combination of SMOTE and ensemble learning models with threshold adjustment provides a more efficient prediction of adverse effects after COVID-19 vaccination, aiding in healthcare decision-making.

This study investigates the use of machine learning (ML) models combined with a Synthetic Minority Over-sampling Technique (SMOTE) and its variants to predict perioperative pressure injuries (PIs) in an imbalanced dataset. PIs are a significant healthcare problem, often leading to prolonged hospitalization and increased medical costs. Conventional risk assessment scales are limited in their ability to predict PIs accurately, prompting the exploration of ML techniques to address this challenge.We utilized data from 7,292 patients admitted to a tertiary care hospital in Shanghai between May 2017 and July 2023, with a final dataset of 2,972 patients, including 158 with PIs. Seven ML algorithms-Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Extra Trees (ET), K-Nearest Neighbors (KNN), and Decision Trees (DT)-were used in conjunction with SMOTE, SMOTE+ENN, Borderline-SMOTE, ADASYN, and GAN to balance the dataset and improve model performance.Results revealed significant improvements in model performance when SMOTE and its variants were used. For instance, the XGBoost model hadan AUC of 0.996 with SMOTE, compared to 0.800 on raw data. SMOTE+ENN and Borderline-SMOTE further enhanced the models' ability to identify minority classes. External validation indicatedthat XGBoost, RF, and ET exhibited the highest stability and accuracy, with XGBoost having an AUC of 0.977. SHAP analysis revealed that factors such as anesthesia grade, age, and serum albumin levels significantly influenced model predictions.In conclusion, integrating SMOTE with ML algorithms effectively addressed a data imbalance and improved the prediction of perioperative PIs. Future work should focus on refining SMOTE techniques and exploring their application to larger, multi-center datasets to enhance the generalizability of these findings, and especially for diseaseswith a lowincidence.

In this paper, we propose an attention-enhanced architecture for improved pneumonia detection in chest X-ray images. A unique attention mechanism is integrated with ResNet to highlight salient features crucial for pneumonia detection. Rigorous evaluation demonstrates that our attention mechanism significantly enhances pneumonia detection accuracy, achieving a satisfactory result of 96% accuracy. To address the issue of imbalanced training samples, we integrate an enhanced focal loss into our architecture. This approach assigns higher weights to minority classes during training, effectively mitigating data imbalance. Our model’s performance significantly improves, surpassing that of traditional approaches such as the pretrained ResNet-50 model. Our attention-enhanced architecture thus presents a powerful solution for pneumonia detection in chest X-ray images, achieving an accuracy of 98%. By integrating enhanced focal loss, our approach effectively addresses imbalanced training sample. Comparative analysis underscores the positive impact of our model’s spatial and channel attention modules. Overall, our study advances pneumonia detection in medical imaging and underscores the potential of attention-enhanced architectures for improved diagnostic accuracy and patient outcomes. Our findings offer valuable insights into image diagnosis and pneumonia prevention, contributing to future research in medical imaging and machine learning.

In previous work, imbalanced datasets composed of more benign samples (the majority class) than the malicious one (the minority class) have been widely adopted in Android malware detection. These imbalanced datasets bias learning toward the majority class, so that the minority class examples are more likely to be misclassified. To solve the problem, we propose a new oversampling method called fuzzy–synthetic minority oversampling technique, which is based on fuzzy set theory and the synthetic minority oversampling technique method. As the sample size of the majority class increases relative to that of the minority class, fuzzy–synthetic minority oversampling technique generates more synthetic examples for each minority class examples in the fuzzy region, where the minority examples have a low degree of membership to the minority class and are more likely to be misclassified. Using the new synthetic examples, the classifiers build larger decision regions that contain more minority examples, and they are no longer biased to the majority class. Compared with synthetic minority oversampling technique and Borderline–synthetic minority oversampling technique methods, fuzzy–synthetic minority oversampling technique achieves higher accuracy on both the minority class and the entire datasets.

Customer turnover prediction can assist enterprises in identifying potential lost customers early and formulating marketing strategies to retain them. This paper used telecom enterprise A as an illustrative example for customer turnover prediction. A balanced dataset was obtained through the synthetic minority oversampling technique (SMOTE) algorithm. Feature selection was conducted using the IV value. Additionally, the Inception v1 structure was optimized based on a deep neural network to design a deep convolutional neural network (CNN). Experiments were performed on the dataset of telecom enterprise A and the customer turnover datasets from Kaggle. On the Kaggle datasets, the deep CNN demonstrated superior classification performance compared to conventional approaches such as random forest (RF) and XGBoost. It exhibited a higher recall rate, score, and area under the curve (AUC) value. The dataset of telecom enterprise A enhanced the prediction effectiveness of the deep CNN after processing by the SMOTE algorithm, and a recall rate of 0.97, a score of 0.98, and an AUC value of 0.98 were achieved. These results show the reliability of the deep CNN for customer turnover prediction and its practical applicability.

Class imbalance, where datasets often lack sufficient samples for minority classes, is a persistent challenge in machine learning. Existing solutions often generate synthetic data to mitigate this issue, but they typically struggle with complex data distributions, primarily because they focus on oversampling the minority class while neglecting the relationships with the majority class. To overcome these limitations, we propose the Contrastive Tabular Variational Autoencoder (CTVAE), which integrates conditional Variational Autoencoders with contrastive learning techniques. CTVAE excels at generating high-quality synthetic samples that capture the intricate data distributions of both minority and majority classes. Additionally, it can be seamlessly integrated with variants of the Synthetic Minority Oversampling Technique (SMOTE) for enhanced effectiveness. Experimental results demonstrate that CTVAE substantially improves classification performance on imbalanced datasets, offering a more robust and holistic solution to the class imbalance problem.

Breast cancer remains a leading cause of mortality among women worldwide, with early and accurate diagnosis being critical for effective treatment. Gene expressionprofiling has emerged as a powerful tool for understanding the molecular mechanisms of cancer and for developing predictive models. However, the high dimensionality and class imbalance inherent in gene expression data pose significant challenges for developing robust predictive models.This study aims to develop and evaluate a predictive model for classifying breast cancer subtypes using high-dimensional gene expression data, addressing the challenges of class imbalance. The objective is to improve the accuracy and reliability of breast cancer subtype prediction to facilitate better diagnostic and treatment strategies.A comprehensive dataset of breast cancer gene expression profiles was utilized, comprising numerous gene expression levels across multiple samples. To address class imbalance, resampling techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and Random Under-sampling, were employed. The machine learning algorithms employed included Support Vector Machines (SVM), Random Forests, and Neural Networks. The algorithms were trained and evaluated using cross-validation to identify the most effective model. The performance of these models was assessed based on metrics such as accuracy, precision, recall, and F1-score. The results indicate that the use of SMOTE in combination with SVM provided the most balanced and accurate predictions, with an F1-score significantly higher than models without resampling. The Random Forest algorithm also showed promising results, particularly in handling the high-dimensionality aspect of the data.The study demonstrates that addressing class imbalance through advanced resampling techniques can significantly enhance the predictive accuracy of models trained on high-dimensional gene expression data. The findings underscore the potential of machine learning models, particularly SVMs and Random Forests, in improving breast cancer subtype classification.

A novel SMOTE-based resampling technique trough noise detection and the boosting procedure

Oversampling is an efficient technique in dealing with class-imbalance problem. It addresses the problem by reduplicating or generating the minority class samples to balance the distribution between the samples of the majority and the minority class. Synthetic minority oversampling technique (SMOTE) is one of the typical representatives. During the past decade, researchers have proposed many variants of SMOTE. However, the existing oversampling methods may generate wrong minority class samples in some scenarios. Furthermore, how to effectively mine the inherent complex characteristics of imbalanced data remains a challenge. To this end, this paper proposes a parameter-free data cleaning method to improve SMOTE based on constructive covering algorithm. The dataset generated by SMOTE is first partitioned into a group of covers, then the hard-to-learn samples can be detected based on the characteristics of sample space distribution. Finally, a pair-wise deletion strategy is proposed to remove the hard-to-learn samples. The experimental results on 25 imbalanced datasets show that our proposed method is superior to the comparison methods in terms of various metrics, such as F-measure, G-mean, and Recall. Our method not only can reduce the complexity of the dataset but also can improve the performance of the classification model.

Background: Class imbalance is often discussed as a strenuous task in the realm of sentiment analysis. In an imbalanced classification, few minority class instances are unable to provide sufficient information, therefore direct learning from an unbalanced dataset can produce unsatisfactory results. This work aims to address the problem of class imbalance. Methods: At primary level this study uses a novel Synthetic Minority Oversampling Technique (SMOTE) for balancing the dataset and then proposes an ensemble model, named Ensemble Bagging Support Vector Machine (EBSVM) for opinion mining. To measure the performance of the particular approach, numerous analyses are conducted on both imbalanced and balanced datasets. Then the work compares the effectiveness of the suggested model with three base classifiers (Nave Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM)). The customer reviews for restaurants is chose as the dataset for this work. Accuracy, precision, recall and F-measure are used as metrics for evaluation. Findings: According to the results, the suggested EBSVM model excels all other individual classifiers with the imbalanced and SMOTE balanced dataset. The balanced EBSVM classifier improves the imbalanced EBSVM Classifier in terms of accuracy. Precision, recall and F-measure of the minority class in the imbalanced classifiers have been improved in balanced Classifiers. Novelty: The performance of opinion mining classifiers for imbalanced and balanced datasets is evaluated in this paper. The work examines not only general opinions, but also specific aspects such as food, service, ambiance, quality, and price. Comparing the suggested model with existing classification algorithms in the literature, it has found that it outperformed the other models. Keywords: Bagging; Accuracy; Ensemble; Precision; Recall; Fmeasure

Unbalanced datasets are a common problem in supervised machine learning. It leads to a deeper understanding of the majority of classes in machine learning. Therefore, the machine learning model is more effective at recognizing the majority classes than the minority classes. Naturally, imbalanced data, such as disease data and data networking, has emerged in real life. DDOS is one of the network intrusions found to happen more often than R2L. There is an imbalance in the composition of network attacks in Intrusion Detection System (IDS) public datasets such as NSL-KDD and UNSW-NB15. Besides, researchers propose many techniques to transform it into balanced data by duplicating the minority class and producing synthetic data. Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) algorithms duplicate the data and construct synthetic data for the minority classes. Meanwhile, machine learning algorithms can capture the labeled data's pattern by considering the input features. Unfortunately, not all the input features have an equal impact on the output (predicted class or value). Some features are interrelated and misleading. Therefore, the important features should be selected to produce a good model. In this research, we implement the recursive feature elimination (RFE) technique to select important features from the available dataset. According to the experiment, SMOTE provides a better synthetic dataset than ADASYN for the UNSW-B15 dataset with a high level of imbalance. RFE feature selection slightly reduces the model's accuracy but improves the training speed. Then, the Decision Tree classifier consistently achieves a better recognition rate than Random Forest and KNN.