IMPLEMENTATION OF BALANCING DATA METHOD USING SMOTETOMEK IN DIABETES CLASSIFICATION USING XGBOOST

Diabetes and obesity reach epidemic proportions all over the world, while the effects of insulin resistance and its consequences are gaining prominence. The article derives the effects of insulin on gestational Pima Indian heritage females with minimum 21 years old. It is identified herein that mean insulin level for gestational women (GW) is positively linked with glucose level (P=0.0055), while it is negatively linked with age (P=0.0095). Mean insulin level for GW is independent of pregnancies (P=0.1866) and diabetes pedigree function (DPF) (P=0.5321), while it is partially positively linked with their joint interaction effect Pregnancies*DPF (P=0.0864). It is positively linked with blood pressure (BP) (P<0.0001) and triceps skin-fold thickness (TST) (P<0.0001), while it is negatively linked with the joint interaction effects BP*TST (P<0.0001) and DPF*TST (P<0.0001). In addition, mean insulin level is negatively linked with body mass index (BMI) (P=0.0001), while it is positively partially linked with the interaction effect BMI*DPF (P=0.1312). Variance of insulin level is positively linked with pregnancies (P=0.0184) and age (P=0.0027), while it is negatively linked with their joint interaction effect Pregnancies*Age (P=0.0418). Also variance of insulin level is partially negatively linked with BMI (P=0.0738) and glucose level (P=0.1439), while it is partially positively linked with their joint interaction effect BMI*Glucose (P=0.1472). Further, variance of insulin level is negatively linked with DPF (P=0.0309). It is concluded that for GW, insulin level increases with the increase of glucose level, BP, TST, and the interaction effects Pregnancies*DPF & BMI*DPF, and it decreases with the increase age, BMI, and the interaction effects BP*TST & DPF*TST. GW should be careful on her glucose level, BMI, BP & TST.

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.

An overlapping minimization-based over-sampling algorithm for binary imbalanced classification

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.

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.

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.

Bitcoin users are guaranteed to be anonymous, increasing the number of cryptocurrency trading related to crimes and fraudulent activities. While most studies about detecting illegal transactions try to distinguish trading patterns and classify them from legitimate ones, classification performance is poor since the class distributions of transaction data are highly imbalanced. In general, the Synthetic Minority Over-sampling TEchnique (SMOTE) is used to deal with class-imbalanced data, but SMOTE has a problem that it does not fully represent the diversity of the data. In this paper, we introduce another oversampling technique using Generative Adversarial Networks (GAN) to generate artificial training data for classification model. In order to verify similarity between artificial data and the actual one, oversampled dataset is evaluated with a classification model using XGBoost algorithm. We show classification performance is improved on average with synthetic data generated by both SMOTE and well-designed GAN model.

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.

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.

Skewed class proportions in real-world datasets present a challenge for machine learning algorithms, as they have a tendency to correctly categorize the majority class while incorrectly classifying the minority class. Such classification disparities hold significant implications, particularly in predictive scenarios involving minority groups, where misclassifying minority instances could lead to adverse outcomes. To tackle this, class imbalance learning has gained attention, with the Synthetic Minority Oversampling Technique (SMOTE) being a notable approach that addresses class imbalance by generating synthetic instances for the minority class based on their feature space neighbors. Despite its effectiveness and simplicity, SMOTE is known to suffer from a noise propagation issue where noisy and uninformative samples are introduced. While various SMOTE variants, including hybrids with undersampling, have been developed to tackle this problem, identifying noisy samples in complex real-world datasets remains a challenge. To address this, our study introduces a new SMOTE-based hybrid approach called SMOTE-centroid displacement-based k-NN (SMOTE-CDNN). SMOTE-CDNN employs centroid displacement for class prediction, which is more robust against noisy data. After SMOTE is applied, noise instances are detected and removed for clearer decision boundaries if their labels predicted by our centroid displacement-based k-NN algorithm are different from the real ones. While our experiments on 24 imbalance datasets demonstrate the resilience and efficiency of our proposed algorithm, which outperforms state-of-art resampling algorithms with various classification models, we acknowledge the need for further investigation into specific dataset characteristics and classification scenarios to determine the generalizability of our approach.

The Industry 4.0 revolution is impacting manufacturing companies, which need to adopt more data intelligence processes in order to compete in the markets they operate. In particular, quality control is a key manufacturing process that has been addressed by Machine Learning (ML), aiming to improve productivity (e.g., reduce costs). However, modern industries produce a tiny portion of defective products, which results in extremely unbalanced datasets. In this paper, we analyze recent big data collected from a major automotive assembly manufacturer and related with the quality of eight products. The eight datasets include millions of records but only a tiny percentage of failures (less than 0.07%). To handle such datasets, we perform a two-stage ML comparison study. Firstly, we consider two products and explore four ML algorithms, Random Forest (RF), two Automated ML (AutoML) methods and a deep Autoencoder (AE), and three balancing training strategies, namely None, Synthetic Minority Oversampling Technique (SMOTE) and Gaussian Copula (GC). When considering both classification performance and computational effort, interesting results were obtained by RF. Then, the selected RF was further explored by considering all eight datasets and five balancing methods: None, SMOTE, GC, Random Undersampling (RU) and Tomek Links (TL). Overall, competitive results were achieved by the combination of GC with RF.

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.

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.

Negative prognostic factors and clinical improvement prediction modeling for extracorporeal shockwave therapy in calcific shoulder tendinitis using artificial intelligence techniques.

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.