Synthetic Minority Over-sampling Technique (SMOTE) for handling imbalanced data in poverty classification

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.

<p>Problem in imbalanced data is very common in classification or identification. The problem is raised when the number of instances of one class far exceeds the other. In the previous research, our DNA barcode-based Identification System of Tuna and Mackerel was developed in imbalanced dataset. The number of samples of Tuna and Mackerel were much more than those of other fish samples. Therefore, the accuracy of the classification model was probably still in bias. This research aimed at employing Synthetic Minority Oversampling Technique (SMOTE) to yield balanced dataset. We used kmers frequencies from DNA barcode sequences as features and Support Vector Machine (SVM) as classification method. In this research we used trinucleotide (3-mers) and tetranucleotide (4-mers). The training dataset was taken from Barcode of Life Database (BOLD). For evaluating the model, we compared the accuracy of model using SMOTE and without SMOTE in order to classify DNA barcode sequences which is taken from Department of Aquatic Product Technology, Bogor Agricultural University. The results showed that the accuracy of the model in the species level using SMOTE was 7% and 13% higher than those of non-SMOTE for trinucleotide (3-mers) and tetranucleotide (4-mers), respectively. It is expected that the use of SMOTE, as one of data balancing technique, could increase the accuracy of DNA barcode based fish classification system, particularly in the species level which is difficult to be identified.</p>

Regression analysis is a statistical method used to examine the functional relationship between two or more independent variables and a dependent variable. One of the regression methods designed to handle censored data or data with significant zero values is Tobit regression. This study aims to model household expenditures on egg and milk consumption in Bengkulu City using Tobit regression and to identify the factors influencing these expenditures. The data were obtained from the 2022 National Socioeconomic Survey, with a total sample of 1,170 households. The Tobit regression model was chosen because most household expenditure data had zero values, indicating censored data characteristics. This study identified several factors affecting expenditures on egg and milk consumption, such as the household head's education level, the number of household members, and the household head's employment sector. The results showed that the education level of the household head (elementary, junior high, and high school), the number of household members, and the household head's employment in agriculture and trade sectors had significant impacts on household expenditures for egg and milk consumption. The education level of the household head and their employment sector had a negative relationship, while the number of household members showed a positive relationship with these expenditures. The Tobit regression model successfully modeled household expenditures with adequate accuracy, as indicated by a Mean Absolute Percentage Error (MAAPE) of 1.38%.

Imbalanced data is a significant challenge in sentiment analysis, as it often impacts the performance of machine learning models. This study applies the Naïve Bayes algorithm, enhanced with the Synthetic Minority Oversampling Technique (SMOTE), to address class imbalance in user reviews of the by.U application. Using the Knowledge Discovery in Databases (KDD) framework, the research involves data selection, preprocessing (text cleaning, normalization, stemming), transformation using TF-IDF, and train-test data splitting. SMOTE is applied to the training data to improve minority class representation, while Naïve Bayes performs sentiment classification. Model evaluation using cross-validation demonstrates that SMOTE increases accuracy from 84.42% to 85.83%. These results underscore the effectiveness of integrating SMOTE with Naïve Bayes in addressing imbalanced data, offering meaningful insights into user sentiment and aiding the development of improved features for the by.U application.

The performance of any classification algorithm is determined not only by the quality of the classifier itself, but also the quality of data to be used. Most data are imbalanced, meaning the different class instances have varying ratios which poses a challenge for classification performance. Synthetic Minority Oversampling Technique (SMOTE) algorithm is an oversampling technique that deals with data imbalance by generating synthetic data instances by linearly interpolating between the minority class instances. However, the SMOTE Algorithm has certain limitations as it has a potential to overfit the classification model. The proposed method utilizes a triangle centroid method for the synthesis of new minority class instances. Heron's formula will also be used for the proposed method to be able to determine the distribution density of the data instances, as well as use a triangular noise mitigation technique to reduce the propagation of noisy data instances. Results of simulation on publicly available medical datasets show that Heron-Centroid SMOTE improved SMOTE across all performance metrics and beating both ‘Imbalanced’ tests and SMOTE on most datasets, with mean F-score of 0.5792, and an average G-mean of 0.6398.

Synthetic Minority Oversampling Technique (SMOTE) is a preferable method used to solve the imbalanced data classification issues. However, its efficiency for resolving the issues of minority sample classification still need to be improved. In order to balance its value and shortcome, we designed a perfected algorithm called “C-K-SMOTE’', which is a mixture clustering algorithm of the Canopy and K-means. For the final purpose of obtaining an approximately balanced data, first we use Canopy to achieve the approximate clustering, then use the K-means to obtain the accurate clustering, and after that we apply the SMOTE to increase the number of minority samples. The referential imbalanced data sets used in the article are selected from KEEL (Knowledge Extraction on Evolutionary Learning). By adopting random forest disaggregated model to carry experiments, SMOTE's efficiency of balancing the imbalanced databases is verified.

An Experimental Assessment of Random Forest Classification Performance Improvisation with Sampling and Stage Wise Success Rate Calculation

Diagnosing a parasitic disease is a very difficult job in clinical practice. In this study, we constructed a machine learning model for diagnosis prediction using patient information. First, we diagnosed whether a patient has a parasitic disease. Next, we predicted the proper diagnosis method among the six types of diagnostic terms (biopsy, endoscopy, microscopy, molecular, radiology, and serology) if the patient has a parasitic disease. To make the datasets, we extracted patient information from PubMed abstracts from 1956 to 2019. We then used two datasets: the prediction for parasite-infected patient dataset (N = 8748) and the prediction for diagnosis method dataset (N = 3780). We then compared four machine learning models: support vector machine, random forest, multi-layered perceptron, and gradient boosting. To solve the data imbalance problem, the synthetic minority over-sampling technique and TomekLinks were used. In the parasite-infected patient dataset, the random forest, random forest with synthetic minority over-sampling technique, gradient boosting, gradient boosting with synthetic minority over-sampling technique, and gradient boosting with TomekLinks demonstrated the best performances (AUC: 79%). In predicting the diagnosis method dataset, gradient boosting with synthetic minority over-sampling technique was the best model (AUC: 87%). For the class prediction, gradient boosting demonstrated the best performances in biopsy (AUC: 88%). In endoscopy (AUC: 94%), molecular (AUC: 90%), and radiology (AUC: 88%), gradient boosting with synthetic minority over-sampling technique demonstrated the best performance. Random forest demonstrated the best performances in microscopy (AUC: 82%) and serology (AUC: 85%). We calculated feature importance using gradient boosting; age was the highest feature importance. In conclusion, this study demonstrated that gradient boosting with synthetic minority over-sampling technique can predict a parasitic disease and serve as a promising diagnosis tool for binary classification and multi-classification schemes.

Breast cancer diagnosis from mammogram scans is challenged by the scarcity of balanced datasets and the complexity of medical image interpretation. Existing systems predominantly utilize a deep learning framework combining ResNet50 for feature extraction and the Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance. While these methods achieve high accuracy on balanced datasets (99%) and reasonable results on imbalanced data (90%), they depend heavily on pre-trained models like VGG16 and ResNet50, which limits adaptability to diverse imaging modalities. Moreover, computational demands and synthetic data generation methods such as SMOTE may not fully capture real-world variability, constraining deployment in resource-limited settings and affecting robustness. To address these limitations, the proposed system introduces an enhanced deep learning architecture incorporating domain-specific pretraining, lightweight model design, and advanced data augmentation techniques beyond SMOTE. This framework aims to improve generalization, computational efficiency, and interpretability through novel visualization tools. The benefits include more accurate, reliable, and accessible breast cancer classification across diverse datasets and clinical environments, offering a promising advancement for early detection and diagnostic support in breast cancer care.

Imbalance data is common in real-world applications like text categorization, face recognition for gender classification, medical diagnosis, fraud detection, oil-spills detection of satellite images. Most of the algorithms in machine learning are focusing on classification of majority class while ignoring or misclassifying minority sample. The minority samples are those that rarely occur but very important. It is commonly agreed that standard classifiers such as neural networks, support vector machines, and C4.5 are heavily biased in recognizing mostly the majority class since they are built to achieve overall accuracy to which the minority class contributes very little. In this study, we demonstrate how the synthetic minority over-sampling technique (SMOTE) can significantly improve the imbalance problem in gender classification from the data-level perspective. Hu’s moment of the face images was generated as the numerical descriptors with different imbalance ratio and classified using a supervised decision tree (J48) algorithm. The results show that prior to preprocessing the data with SMOTE, the minority group was severely misclassified as the majority group. Our claims are confirmed through the application of SMOTE in reducing the imbalance effects before inducing the decision tree.

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.

The decision to raise the Value Added Tax (VAT) rate to 12% in 2025 has elicited diverse public responses, especially on the social media platform X (previously Twitter). This study seeks to categorize public attitudes regarding the policy utilizing two text classification algorithms: Support Vector Machine (SVM) and Multinomial Naïve Bayes. The Synthetic Minority Over-sampling Technique (SMOTE) was applied to the training data to address class imbalance in the sentiment data (positive, negative, neutral). Data were gathered via web crawling using terms relevant to the VAT rise issue, then underwent preprocessing, TF-IDF transformation, and division into two scenarios (80:20 and 70:30). The models' performance was assessed using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC) metrics. The findings indicate that the SVM method without SMOTE achieved optimal performance, with an accuracy of 90.52% and an AUC of 0.860. Simultaneously, the Naïve Bayes algorithm showed improved performance after SMOTE, especially in recall and F1-score, while its overall performance remained inferior to that of SVM. Consequently, SVM is considered more reliable for sentiment classification tasks, although SMOTE has demonstrated efficacy in improving Naïve Bayes performance, particularly for addressing imbalanced datasets.

The inauguration of the President and Vice President of Indonesia for the 2024-2029 period has drawn significant public attention, reflecting widespread political and societal interest. This study aims to optimize sentiment analysis of public opinion on X (formerly Twitter) regarding the inauguration by enhancing the Naïve Bayes Classifier (NBC) with the Synthetic Minority Over-sampling Technique (SMOTE). Addressing the issue of class imbalance in sentiment data, the research demonstrates how SMOTE improves classification robustness. The methodology includes data crawling from X, preprocessing involving tokenization, stemming, and TF-IDF feature extraction, and sentiment labeling using TextBlob. Sentiment classification is conducted with NBC, evaluated under conditions with and without SMOTE. Metrics such as accuracy, precision, recall, and F1-score are utilized to assess performance. Results indicate that the application of SMOTE increases the accuracy of NBC from 98% to 99%, with precision improving from 0.98 to 1 and recall maintaining high levels (0.99). This 1% accuracy enhancement underscores the significance of addressing class imbalance for reliable sentiment analysis. The findings contribute to a better understanding of public sentiment during critical political events and highlight the effectiveness of SMOTE in improving text classification tasks. This research provides valuable insights into leveraging machine learning techniques for analyzing imbalanced datasets, offering implications for both academic and practical applications in sentiment analysis and political studies.

Imbalanced data is a challenge for classification models. It reduces the overall performance of traditional learning algorithms. Besides, the minority class of imbalanced datasets is misclassified with a high ratio even though this is a crucial object of the classification process. In this paper, a new model called the Lasso-Logistic ensemble is proposed to deal with imbalanced data by utilizing two popular techniques, random over-sampling and random under-sampling. The model was applied to two real imbalanced credit data sets. The results show that the Lasso-Logistic ensemble model offers better performance than the single traditional methods, such as random over-sampling, random under-sampling, Synthetic Minority Oversampling Technique (SMOTE), and cost-sensitive learning.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

This study examined factors that influenced households with non-agriculture enterprises to borrow funds as capital for their enterprises in Malawi. Additionally, the study aimed at classifying households as borrowers and non-borrowers, but also classified these households according to their liquidity status in which credit could be a component. The primary data for this study was sourced from the Integrated Household Panel Survey (IHPS) conducted in 2013 by the National Statistics Office of Malawi. Out of 4000 households that formed the sample, 1249 households were reported to own an enterprise; representing 31.23% of the overall sample. The logit model was used in this study and results showed that borrowing was influenced by age of household head, size of household, poverty status of household and enterprise sector. Furthermore, liquidity status was influenced by education attained by household head and poverty status of household. Poverty status of the households was found to influence both borrowing and liquidity non-constraint of households in Malawi. The paper shows the need to focus on poor households that are unable to support their enterprises with personal savings. In addition, policy makers must design programs that will provide credit to poor households operating manufacturing type of enterprises.