Synthetic minority oversampling using edited displacement-based [formula omitted]-nearest neighbors

Sampling technique for noisy and borderline examples problem in imbalanced classification

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.

A new approach to construct the classifiers from imbalanced datasets is proposed by combining SMOTE (synthetic minority over-sampling technique) and Biased-SVM (biased support vector machine) approaches. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ldquonormalrdquo examples with only a small percentage of ldquoabnormalrdquo or ldquointerestingrdquo examples. The cost of misclassifying an abnormal (interesting) example into a normal example is often much higher than that of the reverse error. It was known as a means of increasing the sensitivity of a classifier to the minority class using SMOTE over-sampling in minority class. But in this paper, it gives a good means of increasing the sensitivity of a classifier to the minority class by using SMOTE approaches within support vectors. As for support vector over-sampling, this paper proposes two different over-sampling algorithms to deal with the support vectors being over-sampled by its neighbors from the k nearest neighbors, not only within the support vectors but also within the entire minority class. Some experimental results confirms that the proposed combination approach of SMOTE and biased-SVM can achieve better classifier performance.

Anomaly detection in medical imaging is pivotal for early diagnosis and treatment planning. However, the inherent class imbalance in medical datasets poses significant challenges, often leading to biased models that underperform on minority classes. This study investigates the integration of the Synthetic Minority Over-sampling Technique (SMOTE) with various machine learning and deep learning models to enhance anomaly detection in medical images. By applying SMOTE to balance datasets and evaluating its impact across multiple models, we demonstrate improved detection accuracy, sensitivity, and specificity. The findings underscore the efficacy of SMOTE in addressing class imbalance, thereby enhancing the reliability of anomaly detection systems in medical imaging.

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.

This study employs machine learning models to explore stock price prediction for Tenaga Nasional Berhad (TNB), Malaysia’s primary electricity provider. It addresses the limitations of previous studies by incorporating various input variables, including the stock market, technical, financial, and economic data. This study also tackles the issue of imbalanced class distribution due to small datasets of stock market data by generating synthetic data using Synthetic Minority Over-Sampling Technique (SMOTE) and Generative Adversarial Network-Synthetic Minority Over-Sampling Technique (GAN-SMOTE) techniques. The performance of four classifier models (random forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN)) is evaluated without any synthetic data and with synthetic data generated. The SMOTE-ANN model is the best-performing model, exhibiting superior accuracy of 93%, F1-Score of 92%, precision of 90%, recall of 94%, and specificity of 92%. Overall, this research provides valuable insights into TNB stock price movements, offers a solution for imbalanced class distribution, and identifies the top-performing model for predicting TNB stock price movement. These findings are relevant to investors, analysts, and organisations in the utility sector.

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 that has class imbalance. The method was evaluated using K-NN. The experimental results show that the hybrid approach can improve the accuracy of K-NN in predicting the minority class compared to the conventional approach. It shows that adjusting these parameters can significantly affect the performance of the hybrid approach. In this study, providing the highest accuracy in SMOTE with K-1 neighbors resulted in a 100% improvement in accuracy, followed by a 97% improvement with K-2, and a 93% accuracy with K-3. On the other hand, the undersampling approach using NearMiss showed 100% accuracy improvement with K-1, followed by 74% improvement with K-2, and 76% accuracy with K-3. In conclusion, the use of SMOTE proved to be more consistent in improving accuracy with higher K values. In this case, it is important to consider various parameters in choosing the right resampling technique to handle data imbalance.

Textual data suffers from two main problems, large number of features and class imbalance. Many conventional approaches and their variants exist in literature to solve both these problems. The classic synthetic minority oversampling technique (SMOTE) is the most explored technique for balancing the dataset. We introduced a new algorithm to balance the dataset, named distributed SMOTE (D_SMOTE), which overcomes the problem of lack of density and reducing the formation of small disjuncts. Further, another problem handled is the large number of features or high-dimensionality. To solve high-dimensionality, a novel feature selection technique is introduced known as modified biogeography-based optimization (M_BBO). The proposed model, M_BBO, performs modification in ranking of variables using feature weighting algorithm rather than randomly ranking. We have proposed two new expressions in D_SMOTE and one new expression in M_BBO. The extensive experimental results are computed out on four text classification datasets with four machine learning classifiers. The results are concluded using three performance measures, area under curve, G-mean, and F1-score. Our empirical and statistical observation for four class-imbalanced datasets shows that the proposed D_SMOTE outperforms the other similar oversampling technique. We have also compared our proposed algorithm, M_BBO+D_SMOTE, with other models on 17 imbalanced text classification datasets. Our model outperformed the other models in 14 datasets. We have also compared our model with bidirectional encoder representations from transformers. To validate the experimental analysis, statistical Friedman test is employed.

Oversampling ratio N and the minority class’ nearest neighboring number k are key hyperparameters of synthetic minority oversampling technique (SMOTE) to reconstruct the class distribution of dataset. No optimal default value exists there. Therefore, it is of necessity to discuss the influence of the output dataset on the classification performance when SMOTE adopts various hyperparameter combinations. In this paper, we propose a hyperparameter optimization algorithm for imbalanced data. By iterating to find reasonable N and k for SMOTE, so as to build a balanced and high-quality dataset. As a result, a model with outstanding performance and strong generalization ability is trained, thus effectively solving imbalanced classification. The proposed algorithm is based on the hybridization of simulated annealing mechanism (SA) and particle swarm optimization algorithm (PSO). In the optimization, Cohen’s Kappa is used to construct the fitness function, and AdaRBFNN, a new classifier, is integrated by multiple trained RBF neural networks based on AdaBoost algorithm. Kappa of each generation is calculated according to the classification results, so as to evaluate the quality of candidate solution. Experiments are conducted on seven groups of KEEL datasets. Results show that the proposed algorithm delivers excellent performance and can significantly improve the classification accuracy of the minority class.

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.

Public complaint services are essential for improving government service quality by providing a direct channel for citizens to report issues. In Karawang Regency, the Tanggap Karawang (TANGKAR) platform serves this function; however, the manual classification of complaints causes delays and potential misrouting, especially due to the highly imbalanced distribution of complaint categories. This study develops an automatic classification model for public complaints in eight categories economy, education, health, social, infrastructure, security, environment, and transportation by integrating Term Frequency–Inverse Document Frequency (TF–IDF), Multinomial Naive Bayes, and Synthetic Minority Oversampling Technique (SMOTE). This integration addresses domain-specific class imbalance challenges, combining the computational efficiency of Naive Bayes, the feature representation strength of TF–IDF, and the improved minority class recognition from SMOTE. A dataset of 800 complaint records from TANGKAR underwent preprocessing, including cleaning, case folding, normalization, tokenizing, stemming, and stopword removal. TF–IDF with unigram and bigram features was used for feature extraction, followed by classification under two scenarios: original unbalanced data and balanced data via SMOTE. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. The model achieved 85.09% accuracy without SMOTE and 83.40% with SMOTE, with notable improvement in detecting minority categories after balancing. Although overall accuracy slightly decreased, SMOTE enhanced equitable prediction across all categories. This approach advances current public complaint classification methods by adapting to the linguistic diversity and uneven category distribution in actual e-government data, supporting faster and more accurate decision-making in public complaint management systems.

Binary datasets are considered imbalanced when one of their two classes has less than 40% of the total number of the data instances (i.e., minority class). Existing classification algorithms are biased when applied on imbalanced binary datasets, as they misclassify instances of minority class. Many techniques are proposed to minimize the bias and to increase the classification accuracy. Synthetic Minority Oversampling Technique (SMOTE) is a well-known approach proposed to address this problem. It generates new synthetic data instances to balance the dataset. Unfortunately, it generates these instances randomly, leading to the generation of useless new instances, which is time and memory consuming. Different SMOTE derivatives were proposed to overcome this problem (such as Borderline SMOTE), yet the number of generated instances slightly changed. To overcome such problem, this paper proposes a novel approach for generating synthesized data instances known as Hybrid Clustered Affinitive Borderline SMOTE (HCAB-SMOTE). It managed to minimize the number of generated instances while increasing the classification accuracy. It combines undersampling for removing majority noise instances and oversampling approaches to enhance the density of the borderline. It uses k-means clustering on the borderline area and identify which clusters to oversample to achieve better results. Experimental results show that HCAB-SMOTE outperformed SMOTE, Borderline SMOTE, AB-SMOTE and CAB-SMOTE approaches which were developed before reaching HCAB-SMOTE, as it provided the highest classification accuracy with the least number of generated instances.

Classification models trained on imbalanced datasets tend to be biased towards the majority category, resulting in reduced accuracy for minority categories. A common approach to address this problem is to generate artificial data for underrepresented categories. The Synthetic Minority Over-sampling Technique (SMOTE) algorithm and its variants are widely used for this purpose. In this paper, we propose a modification to the data generation mechanism called Iteration-based SMOTE (ISMOTE). Unlike SMOTE, the ISMOTE algorithm trains the data for multiple iterations. In each iteration, the model generates new samples in the vicinity of appropriately misclassified data. These new samples are then fed into the classification model, thus improving classification accuracy over the course of multiple iterations. We compare the performance of ISMOTE with SMOTE and other commonly used oversampling algorithms. Our empirical results demonstrate that ISMOTE significantly improves the quality of the generated data compared to other oversampling methods. Additionally, we conduct experiments to verify the effect of parameters on the model and provide suggestions for choosing appropriate values to improve performance.

Nowadays, information technology especially machine learning has been used to evaluate the feasibility of debtors. One of the challenges in this classification model is the occurrence of imbalanced datasets, especially in the German Credit Dataset. Another challenge is developing an optimal model for evaluating debtor eligibility. Based on these challenges, this study aims to develop an optimal model for evaluating debtor eligibility on the German Credit Dataset, using the decision trees, k-Nearest Neighbor (k-NN) and Synthetic Minority Oversampling Technique (SMOTE). SMOTE and k-NN is used to overcome challenges regarding imbalanced datasets. While the decision tree are applied to produce a debtor classification model. In general, the steps taken are preparing datasets, pre-processing data, dividing datasets, oversampling with SMOTE, and classification models using decision trees, and testing. Model performance evaluation is represented by accuracy values obtained from the confusion matrix and area under curve (AUC) values generated by the Receiver Operating Characteristic (ROC). Based on the tests that have been carried out, the best accuracy value in the test is obtained at 73.00% and the AUC value is 0.708, in parameters k = 3 and Max-Depth = 25. Based on the analysis produced, the proposed model can improve performance compared to if the dataset is not applied SMOTE.

In this research, XGBoost algorithm and the SMOTETomek approach are employed with the objective of enhancing the accuracy of diabetes classification. The study utilises 2,000 patient data points, comprising demographic and medical information, sourced from Kaggle. The dataset employed in this study comprises a number of variables, including pregnancies, glucose levels, blood pressure, skin thickness, insulin levels, Body Mass Index (BMI), diabetes pedigree function, age, and an outcome variable. The latter is a binary classification label, taking on the values 0 and 1. A value of 0 indicates that the patient is not affected by diabetes, whereas a value of 1 indicates that the patient has diabetes. Diabetes represents a significant public health concern in Indonesia. A significant challenge in this study was the imbalanced nature of the dataset, which included a disproportionate number of non-diabetic samples relative to diabetic samples. To address this class imbalance, the researchers employed the SMOTETomek method. SMOTETomek integrates the SMOTE (Synthetic Minority Over-sampling Technique) and Tomek links algorithms to oversample the minority class and remove borderline samples, thereby balancing the class distributions. The SMOTETomek method achieved higher accuracy (95.01%) than SMOTE and the original data (both 92.13%), highlighting the benefits of combining SMOTE with Tomek Links for XGBoost. During testing, SMOTETomek slightly reduced the minority class accuracy (0.97 vs. 0.99 for SMOTE and original data) but maintained strong F1-score and precision, indicating effective handling of data imbalance despite minor trade-offs.