PREmbed: Balancing Conditional Generative Models with Embedding Pretraining and Regularization

A conditional variational autoencoding generative adversarial networks with self-modulation for rolling bearing fault diagnosis

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

ECG signal generation based on conditional generative models

The challenges related to handling of the gigantic imbalanced data volumes are incredible and has set a new trail for its efficient processing. The inventive prospects contained by these huge imbalanced data sets have posed a priority of concern in recent research avenues. The several applications handling imbalanced Big Data sets have noted significance for precise classification while determining unidentified values from these data sets. Traditional classifiers are not able to discourse the imbalance of class distribution among the data samples. A class having fewer samples indicates difficulty in learning, whereas it points to a notable drop in the performance. Recent studies demonstrate that the classifier independent set of oversampling techniques are more capable to efficiently handle the issues raised in imbalanced data sets. An enhanced oversampling technique, viz., Minority–Majority Mix mean Oversampling Technique (MMMmOT), improving classification performance is discussed in detail in this paper. An appropriate consideration of majority as well as minority samples is planned to generate the synthetic samples. The proposed technique is investigated encircling data sets mainly from the UCI repository over Apache Hadoop. Furthermore, the stimulus of maintaining the imbalance ratio with better oversampling instances from the generated pool is analyzed. The results of classification performance are recognized using standard parameters like F-Measure and area under the curve. The achieved experimental outcomes clearly exhibit the preeminence of the presented technique over the traditional techniques.

Diabetic retinopathy (DR) is a sense-based disorder particularly related to eye. The advanced stage leads to reducing of blood vessels in the eye. DR causes blurred vision in initial stage, and it may lead to blindness at advance stages if it is not identified properly in early stage. Conventional approaches have been applied to detect DR but cannot code the intricate underlying features. In this paper, we proposed the approach based on texture analysis and used deep convolution neural network (DCNN) models with an ensemble technique to detect all the stages of DR by using balanced and imbalanced datasets. The dataset is downloaded from Kaggle and used to train the models. Only balanced dataset was used to train both models. Testing was performed on these models with balanced and imbalanced datasets. The result shows that the proposed models classify all the stages of DR, differentiate with other diseases and perform better compared to the other approaches.KeywordsEyesTextureDiabetic retinopathyClassificationCNNVGGResNet

The problem of imbalanced data has a heavy impact on the performance of learning models. In the case of an imbalanced text dataset, minority class data are often classified to the majority class, resulting in a loss of minority information and low accuracy. Thus, it is a serious challenge to determine how to tackle the high imbalance ratio distribution of datasets. Here, we propose a novel classification method for learning tasks with imbalanced test data. It aims to construct a method for data preprocessing that researchers can apply to their learning tasks with imbalanced text data and save the efforts to search for more dedicated learning tools. In our proposed method, there are two core stages. In stage one, balanced datasets are generated using an asymmetric cost-sensitive support vector machine; in stage two, the balanced dataset is classified using the symmetric cost-sensitive support vector machine. In addition, the learning parameters in both stages are adjusted with a genetic algorithm to create an optimal model. A Yelp review dataset was used to validate the effectiveness of the proposed method. The experimental results showed that the proposed method led to a better performance subject to the targeted dataset, with at least 75% accuracy, and revealed that this new method significantly improved the learning approach.

This research introduces a Multistage-Vision Transformer (Multistage-ViT) model for precisely classifying various lung diseases using chest radiographic (CXR) images. The dataset in the proposed method includes four classes: Normal, COVID-19, Viral Pneumonia and Lung Opacity. This model demonstrates its efficacy on imbalanced and balanced datasets by enhancing classifier accuracy through deep feature extraction. It integrates backbone models with the ViT architecture, creating rigorously hybrid configurations compared to their standalone counterparts. These hybrid models utilize optimized features for classification, significantly improving their performance. Notably, the multistage-ViT model achieved accuracies of 99.93% on an imbalanced dataset and 99.97% on a balanced dataset using the InceptionV3 combined with the ViT model. These findings highlight the superior accuracy and robustness of multistage-ViT models, underscoring their potential to enhance lung disease classification through advanced feature extraction and model integration techniques. The proposed model effectively demonstrates the benefits of employing ViT for deep feature extraction from CXR images.

Software defect prediction (SDP) is an essential task for developing quality software, and various models have been developed for this purpose. However, the imbalanced nature of software defect datasets has challenged these models, resulting in decreased performance. To address this challenge, the author has proposed a hybrid machine learning model that combines Synthetic Minority Oversampling Technique (SMOTE) with Support Vector Machine (SVM)—SMOTE-SVM (S-SVM) model. The author has empirically examined SDP using multiple datasets (CM1, PC1, JM1, PC3, KC1, EQ and JDT) from the PROMISE and AEEEM repositories. The experimental study indicates that the S-SVM model involved training and compared with previously developed balanced and imbalanced test datasets using four evaluation metrics: Precision, Recall, F1 score, and Accuracy. For the balanced dataset, the S-SVM model achieved precision values ranging from 70 to 96, recall values ranging from 52 to 94, F1-score values ranging from 67 to 90, and accuracy values ranging from 69 to 98. For the imbalanced dataset, the S-SVM model achieved precision values ranging from 60 to 93, recall values ranging from 64 to 97, F1-score values ranging from 69 to 91, and accuracy values ranging from 67 to 87. The proposed S-SVM model outperforms other models’ ability to classify and predict software defects. Therefore, the hybridisation of SMOTE and SVM improved the model’s ability to categories and predict balanced and imbalanced datasets when sufficient defective and non-defective data is provided.

A synthetic informative minority over-sampling (SIMO) algorithm leveraging support vector machine to enhance learning from imbalanced datasets

An important problem in medical data analysis is imbalance dataset. This problem is a cause of diagnostic mistake. The results of diagnostic affect to life of patients. If a doctor fails in diagnostic of patient who have disease that means he cannot treat patient in timely. However, the problem can be easily solved by adding or removing the data to closely balance for performance of diagnostic in medically. This paper proposed a solution to adjust imbalance dataset by combining Neighbor Cleaning Rule (NCL) and Synthetic Minority Over-Sampling Technique (SMOTE) techniques. The process of work is using NCL technique for removing sample data that are outliers in majority class and SMOTE technique is used for increasing sample data in minority class to closely balance dataset. After that, the balanced medical dataset is classified by Naive Bayes, SMO and KNN algorithm. The experimental results show that the recall rate can be improved from the models that were created from balanced dataset.

Microplastic particle ingestion or inhalation by humans is a problem of growing concern. Unfortunately, current research methods that use machine learning to understand their potential harms are obstructed by a lack of available data. Deep learning techniques in particular are challenged by such domains where only small or imbalanced data sets are available. Overcoming this challenge often involves oversampling underrepresented classes or augmenting the existing data to improve model performance. This paper proposes GANsemble: a two-module framework connecting data augmentation with conditional generative adversarial networks (cGANs) to generate class-conditioned synthetic data. First, the data chooser module automates augmentation strategy selection by searching for the best data augmentation strategy. Next, the cGAN module uses this strategy to train a cGAN for generating enhanced synthetic data. We experiment with the GANsemble framework on a small and imbalanced microplastics data set. A Microplastic-cGAN (MPcGAN) algorithm is introduced, and baselines for synthetic microplastics (SYMP) data are established in terms of Fréchet Inception Distance (FID) and Inception Scores (IS). We also provide a synthetic microplastics filter (SYMP-Filter) algorithm to increase the quality of generated SYMP. Additionally, we show the best amount of oversampling with augmentation to fix class imbalance in small microplastics data sets. To our knowledge, this study is the first application of generative AI to synthetically create microplastics data.

Sentiment analysis is crucial for understanding public opinion, especially in political contexts like the 2024 South Sumatra gubernatorial election. Social media platforms such as Twitter and YouTube provide key sources of public sentiment, which can be analyzed using machine learning to classify opinions as positive, neutral, or negative. However, challenges such as data imbalance and selecting the right model to improve classification accuracy remain significant. This study compares five machine learning algorithms (SVM, Naïve Bayes, KNN, Decision Tree, and Random Forest) and examines the impact of data balancing on their performance. Data was collected via Twitter crawling (140 entries) and YouTube scraping (384 entries), and text features were extracted using CountVectorizer. The models were then evaluated on imbalanced and balanced datasets using accuracy, precision, recall, and F1-score. The Decision Tree and Random Forest models achieved the highest accuracies of 79.22% and 75.32% on imbalanced data, respectively. However, they also exhibited overfitting, as indicated by their near-perfect training performance. Naïve Bayes, on the other hand, demonstrated the lowest accuracy at 54.55% despite achieving high precision, suggesting frequent misclassification, particularly for the minority class. SVM and KNN also struggled with imbalanced data, recording accuracies of 58.44% and 63.64%, respectively. Significant improvements were observed after applying data balancing techniques. The accuracy of SVM increased to 71.43%, and KNN improved to 66.23%, indicating that these models are more stable and effective when class distributions are even. These findings highlight the substantial impact of data balancing on model performance, particularly for methods sensitive to class distribution. While tree-based models achieved high accuracy on imbalanced data, their tendency to overfit underscores the importance of balancing techniques to enhance model generalization.

In the actual working process of the analog circuit, the probability of multiple component failures at the same time is lower than the probability of a single component failure, which makes the single fault data samples and multiple fault data samples tend to show imbalanced characteristics. However, most of the existing data-driven analog circuit diagnosis methods focus on the balance data sample set. Therefore, it is hard to satisfy the needs of fault diagnosis during the actual working of analog circuits. In response to the problems raised above, an analog circuit fault diagnosis method based on enhanced boundary equilibrium generative adversarial network (EBEGAN) is proposed. The generator of boundary equilibrium generative adversarial networks (BEGAN) uses conditional variational auto encoder (CVAE), which can enhance the generated sample quality while ensuring sample diversity. In addition, by introducing the classified loss factor into the loss function, the discriminator has the ability to distinguish the true and false and the type of samples. The experimental results indicate that this study proposes the new method in the situation of imbalanced data, the type of fault in the analog circuit can be accurately identified. compared with the existing analog circuit fault diagnosis methods.

Social media platforms and microblogging websites have gained accelerated popularity during the past few years. These platforms are used for expressing views and opinions about products, personalities, and events. Often during discussions and debates, fights take place on social media platforms which involves using rude, disrespectful, and hateful comments called toxic comments. The identification of toxic comments has been regarded as an essential element for social media platforms. This study introduces an ensemble approach, called regression vector voting classifier (RVVC), to identify the toxic comments on social media platforms. The ensemble merges the logistic regression and support vector classifier under soft voting criteria. Several experiments are performed on the imbalanced and balanced dataset to analyze the performance of the proposed approach. For data balance, the synthetic minority oversampling technique (SMOTE) is used on the imbalanced dataset. Furthermore, two feature extraction approaches are utilized to investigate their suitability such as term frequency-inverse document frequency (TF-IDF) and bag-of-words (BoW). The performance of the proposed approach is compared with several machine learning classifiers using accuracy, precision, recall, and F1-score. Results suggest that RVVC outperforms all other individual models when TF-IDF features are used with SMOTE balanced dataset and achieves an accuracy of 0.97.

Heat shock proteins (HSPs) are ubiquitous in living organisms. HSPs are an essential component for cell growth and survival; the main function of HSPs is controlling the folding and unfolding process of proteins. According to molecular function and mass, HSPs are categorized into six different families: HSP20 (small HSPS), HSP40 (J-proteins), HSP60, HSP70, HSP90, and HSP100. In this paper, improved methods for HSP prediction are proposed—the split amino acid composition (SAAC), the dipeptide composition (DC), the conjoint triad feature (CTF), and the pseudoaverage chemical shift (PseACS) were selected to predict the HSPs with a support vector machine (SVM). In order to overcome the imbalance data classification problems, the syntactic minority oversampling technique (SMOTE) was used to balance the dataset. The overall accuracy was 99.72% with a balanced dataset in the jackknife test by using the optimized combination feature SAAC+DC+CTF+PseACS, which was 4.81% higher than the imbalanced dataset with the same combination feature. The Sn, Sp, Acc, and MCC of HSP families in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.