Self-training convolutional autoencoder for consumer characteristics identification with imbalance datasets

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

CIRA: Class imbalance resilient adaptive Gaussian process classifier

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

One of the more prevalent problems when working with bioinformatics datasets is class imbalance, when there are more instances in one class compared to the other class (es). This problem is made worse because frequently, the class of interest is also the minority class. A possible solution is data sampling, a powerful tool for combating class imbalance by adding or removing instances to make the dataset more balanced. In addition to the choice of including data sampling, one of the most important decisions when applying data is what the final class ratio should be. Commonly, the final class ratio when data is applied is 50:50, however it is an open question whether other ratios are more appropriate for certain imbalanced datasets (all datasets in this paper have 25.16% minority instances or less) where a 50:50 ratio requires extreme modification to the dataset. In this work we compare six different data approaches (feature selection with the pair wise combinations of three data techniques and two final class ratios) with feature selection without data with the goal of determining if the inclusion of data is beneficial and if so, what should be the final class ratio. In order to test the six data approaches and feature selection alone thoroughly, we utilize seven imbalanced and high-dimensional datasets, three feature selection techniques, and six classifiers. Our results show that for a majority of scenarios, random under along with either 35:65 or 50:50 is the best data approach. Statistical analysis shows that there is significant difference between the data approaches. However, despite this, we still recommend using random under along with 35:65 as the final class ratio. This is because of the frequency of both random under and 35:65 being the most frequent top performing data technique and class ratio respectively. Additionally, 35:65 will have fewer negative impacts than 50:50 (less data loss or over fitting, which makes it a better choice if all other factors are equal) and random under is more computationally efficient than any other form of sampling, including no sampling (both by not requiring any internal calculations and by producing a reduced, easier-to-work-with dataset). To our knowledge, this is the most comprehensive work which focuses on the choice of the inclusion and implementation of data with different final class ratios on bioinformatics datasets which exhibit such large levels of class imbalance.

<abstract><p>In the realm of machine learning, where data-driven insights guide decision-making, addressing the challenges posed by class imbalance in datasets has emerged as a crucial concern. The effectiveness of classification algorithms hinges not only on their intrinsic capabilities but also on their adaptability to uneven class distributions, a common issue encountered across diverse domains. This study delves into the intricate interplay between varying class imbalance levels and the performance of ten distinct classification models, unravelling the critical impact of this imbalance on the landscape of predictive analytics. Results showed that random forest (RF) and decision tree (DT) models outperformed others, exhibiting robustness to class imbalance. Logistic regression (LR), stochastic gradient descent classifier (SGDC) and naïve Bayes (NB) models struggled with imbalanced datasets. Adaptive boosting (ADA), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and k-nearest neighbour (kNN) models improved with balanced data. Adaptive synthetic sampling (ADASYN) yielded more reliable predictions than the under-sampling (UNDER) technique. This study provides insights for practitioners and researchers dealing with imbalanced datasets, guiding model selection and data balancing techniques. RF and DT models demonstrate superior performance, while LR, SGDC and NB models have limitations. By leveraging the strengths of RF and DT models and addressing class imbalance, classification performance in imbalanced datasets can be enhanced. This study enriches credit risk modelling literature by revealing how class imbalance impacts default probability estimation. The research deepens our understanding of class imbalance's critical role in predictive analytics. Serving as a roadmap for practitioners and researchers dealing with imbalanced data, the findings guide model selection and data balancing strategies, enhancing classification performance despite class imbalance.</p></abstract>

Even if measuring the outcome of binary classifications is a pivotal task in machine learning and statistics, no consensus has been reached yet about which statistical rate to employ to this end. In the last century, the computer science and statistics communities have introduced several scores summing up the correctness of the predictions with respect to the ground truth values. Among these scores, the Matthews correlation coefficient (MCC) was shown to have several advantages over confusion entropy, accuracy, F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score, balanced accuracy, bookmaker informedness, markedness, and diagnostic odds ratio: MCC, in fact, produces a high score only if the majority of the predicted negative data instances and the majority of the positive data instances are correct, and therefore it results being very trustworthy on imbalanced datasets. In this study, we compare MCC with two other popular scores: Cohen's Kappa, a metric that originated in social sciences, and the Brier score, a strictly proper scoring function which emerged in weather forecasting studies. After explaining the mathematical properties and the relationships between MCC and each of these two rates, we report some use cases where these scores generate different values, which lead to discordant outcomes, where MCC provides a more truthful and informative result. We highlight the reasons why it is more advisable to use MCC rather that Cohen's Kappa and the Brier score to evaluate binary classifications.

Modern manufacturing processes are highly instrumented to capture data at every step along the assembly lines. Such fine-grained data can help manufacturers perform quality control by predicting which manufactured products are at risk of being defective. A predictive analytics system that identifies internal failures in products before they are shipped can result in significant savings for the manufacturer and result in better customer satisfaction. However, predicting internal failures in manufactured products is a challenging machine learning task due to two reasons: (i) the rarity of such failures in modern manufacturing processes, and (ii) the failure of traditional machine learning algorithms to optimize non-convex metrics such as Matthew's Correlation Coefficient (MCC) used to measure performance on imbalanced datasets. In this paper, we present “ImbalancedBayesOpt”, a meta-optimization algorithm that directly maximizes MCC by learning optimal weights for the positive and negative classes. We use Gradient Boosting Machine (GBM) as the base classifier for our meta-optimization algorithm due to its competitive performance on machine learning prediction tasks. Our quantitative evaluation suggests that “ImbalancedBayesOpt” can significantly improve the classification performance of base classifiers on severely imbalanced high-dimensional datasets.

Magnetic resonance imaging (MRI) is a substantial imaging procedure for diagnosing brain tumors. However, brain tumor classification continues challenging due to the unequal distribution of classes within datasets, complicating precise diagnosis and classification. This research focuses on the class imbalance in medical image datasets by proposing a hybrid bio-inspired algorithm for brain tumor classification. A rider optimization and particle rider mutual information-based dendritic-squirrel search algorithm combined with an artificial immune classifier is developed and tested on imbalanced datasets generated from BRATS and SimBRATS. Experimental outcomes are encouraging, For the imbalanced BRATS dataset, the rider optimization- based classifier achieved an accuracy of 94.84%, sensitivity of 92.96%, and specificity of 94.95%. The particle rider mutual information-based classifier outperformed others with 96.25% accuracy, 94.33% sensitivity, and 94.85% specificity. For the imbalanced SimBRATS dataset, the rider optimization-based classifier achieved 94.95% accuracy, 92.05% sensitivity, and 94.04% specificity. The particle rider mutual information-based classifier excelled with 96.35% accuracy, 94.42% sensitivity, and 95.44% specificity. These findings suggest that the proposed algorithm effectively addresses class imbalance in medical image datasets, offering a robust solution for brain tumor classification. The particle rider mutual information-based classifier shows promise for enhancing diagnostic accuracy in clinical settings, demonstrating the efficacy of hybridized bio-inspired algorithms in managing imbalanced datasets.

The performance of classifiers can decline due to irrelevant or non-informative features. Therefore, extracting meaningful high-level features from raw data through effective feature selection and construction is vital. This challenge is particularly significant in bioinformatics, especially for predicting protein-peptide interactions. To address this, we propose IntPPPred, a computational method designed to enhance residue-level prediction of such interactions. IntPPPred leverages evolutionary computation and ensemble learning to build high-level features from selected informative ones, reducing computational complexity and feature space. It first identifies unique and effective features before applying multiple feature constructions using a gravitational search algorithm. This enhances the prediction capability of a stacking-based ensemble classifier, particularly on imbalanced datasets. Experimental results show that IntPPPred improves Matthews correlation coefficient (MCC), F-measure, and precision by at least 1.2%, 19.6%, and 3.9%, respectively, over existing methods. When evaluated on a second dataset, further gains of 5.2%, 24%, 16.7%, and 1.1% were observed in precision, sensitivity, F-measure, and MCC. Additionally, performance consistency between cross-validation and independent test sets confirms the method's robustness. Overall, IntPPPred serves as an effective computational tool that supports experimental research and enhances machine learning performance in protein-peptide interaction prediction.

To surmount the two-class imbalanced problem existing in the breast cancer diagnosis, a hybrid method of ROSE sampling approach with Boosted C5.0 ensemble classifier (R-Boosted C5.0) is proposed. ROSE as the sampling method is utilized to balance the class distribution. Boosted C5.0 is then used as the classifier. To serve this purpose, Wisconsin Breast Cancer Dataset (WBCD), Wisconsin Diagnosis Breast Cancer (WDBC) and three imbalanced datasets have been studied. Assessing by Matthews Correlation Coefficient (MCC), the performance of proposed method on WBCD and WDBC datasets were 98.5% and 93.0%, respectively. The experimental results show that the proposed work outperforms in contrast with the rest of the classifiers. It can be used as a clinical decision support system to assist breast cancer prediction. In practice, the proposed methodology can be further applied to class imbalanced data classification.

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.

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.

The electricity market has been significantly changing in the last decade. The deployment of smart meters is enabling the logging of huge amounts of data relating to the operations of utilities with the potential of being translated into knowledge on consumers and enable personalized energy efficiency programs. This paper proposes an approach for mining characteristics of a residential consumers (income, education and having children) from high-resolution smart meter data using transparent fuzzy models. The system consists in: (1) extraction of comprehensive consumption features from smart meter data, (2) use of fuzzy models in order to estimate the characteristics of consumers, and (3) knowledge extraction from the fuzzy models rules. Accurate estimates of consumer income and education level were not achieved (60 % accuracy), for the presence of children accuracies of over 70 % were achieved. Performance is comparable to the state of the art with the addition of model interpretability and transparency.

The imbalanced dataset is a challenging issue in many classification tasks. Because it leads a machine learning algorithm to poor generalization and performance. The imbalanced dataset is characterized as having a huge difference between the number of samples that contain each class. Unfortunately, various resampling methods are proposed to solve this problem. In our work, we target enhancing the handling of the imbalanced dataset using a new oversampling technique based on generative adversarial neural networks. Our method is benchmarked against the widely used oversampling technique including the synthetic minority oversampling technique (SMOTE), random oversampling technique (ROS), and the adaptive synthetic sampling approach(ADSYN). Additionally, three machine learning algorithms are used for evaluation. The outcome of our experiments on a real-world credit card dataset shows the strong ability of the proposed solution against the competitive oversampling techniques to overcome the imbalanced problem in the European credit card dataset.

Generally, class imbalance is a major problem that occurs with various machine learning problems where there are a large number of instances of one class compared to another class. In this case, it has been found that a typical classification model may be biased towards a particular class and may not be able to classify instances of another class properly. This problem is critical while dealing with various real-world problems. In recent times, Broad Learning System (BLS) has gained attention from many researchers due to its fast training speed and efficiency of the flat network. However, it has been found that the conventional BLS model may not be efficient while dealing with imbalanced data sets because it uses a least squares method. Generally, a least squares method may not be efficient while dealing with imbalanced data sets. This paper proposes an adaptive margin broad learning system (AMBLS) model to improve its efficiency while handling imbalanced data sets in order to address this issue with traditional BLS models. The suggested approach may be more effective when handling imbalanced data sets than traditional BLS models, according to experimental results on a number of benchmark imbalanced data sets. Keywords- Imbalanced Learning; Broad Learning System; Adaptive Margin; Minority Classification; Machine Learning.