Clinical data analysis and forecasting have made substantial contributions to disease control, prevention and detection. However, such data usually suffer from highly imbalanced samples in class distributions. In this paper, we aim to formulate effective methods to rebalance binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat algorithm, and apply them to empower the effects of synthetic minority over-sampling technique (SMOTE) for pre-processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reported in this paper reveal that the performance improvements obtained by the former methods are not scalable to larger data scales. The latter methods, which we call Adaptive Swarm Balancing Algorithms, lead to significant efficiency and effectiveness improvements on large datasets while the first method is invalid. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. The proposed methods lead to more credible performances of the classifier, and shortening the run time compared to brute-force method.

In recent years, the challenge of imbalanced data has become increasingly prominent in machine learning, affecting the performance of classification algorithms. This study proposes a novel data-level oversampling method called Cluster-Based Reduced Noise SMOTE (CRN-SMOTE) to address this issue. CRN-SMOTE combines SMOTE for oversampling minority classes with a novel cluster-based noise reduction technique. In this cluster-based noise reduction approach, it is crucial that samples from each category form one or two clusters, a feature that conventional noise reduction methods do not achieve. The proposed method is evaluated on four imbalanced datasets (ILPD, QSAR, Blood, and Maternal Health Risk) using five metrics: Cohen’s kappa, Matthew’s correlation coefficient (MCC), F1-score, precision, and recall. Results demonstrate that CRN-SMOTE consistently outperformed the state-of-the-art Reduced Noise SMOTE (RN-SMOTE), SMOTE-Tomek Link, and SMOTE-ENN methods across all datasets, with particularly notable improvements observed in the QSAR and Maternal Health Risk datasets, indicating its effectiveness in enhancing imbalanced classification performance. Overall, the experimental findings indicate that CRN-SMOTE outperformed RN-SMOTE in 100% of the cases, achieving average improvements of 6.6% in Kappa, 4.01% in MCC, 1.87% in F1-score, 1.7% in precision, and 2.05% in recall, with setting SMOTE’s neighbors’ number to 5.

This record contains the underlying research data for the publication "High impact bug report identification with imbalanced learning strategies" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/3702In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high-impact bugs are used to refer to the bugs which appear at unexpected time or locations and bring more unexpected effects (i.e., surprise bugs), or break pre-existing functionalities and destroy the user experience (i.e., breakage bugs). Unfortunately, identifying high-impact bugs from thousands of bug reports in a bug tracking system is not an easy feat. Thus, an automated technique that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. Considering that only a small proportion of bugs are high-impact bugs, the identification of high-impact bug reports is a difficult task. In this paper, we propose an approach to identify high-impact bug reports by leveraging imbalanced learning strategies. We investigate the effectiveness of various variants, each of which combines one particular imbalanced learning strategy and one particular classification algorithm. In particular, we choose four widely used strategies for dealing with imbalanced data and four state-of-the-art text classification algorithms to conduct experiments on four datasets from four different open source projects. We mainly perform an analytical study on two types of high-impact bugs, i.e., surprise bugs and breakage bugs. The results show that different variants have different performances, and the best performing variants SMOTE (synthetic minority over-sampling technique) + KNN (K-nearest neighbours) for surprise bug identification and RUS (random under-sampling) + NB (naive Bayes) for breakage bug identification outperform the F1-scores of the two state-of-the-art approaches by Thung et al. and Garcia and Shihab.Supplementary code and data available from GitHub:

A comparison of the RN-SMOTE, SMOTE-Tomek Link, SMOTE-ENN, and the proposed 1CRN-SMOTE methods on the Blood and Health-risk datasets is presented, based on various classification metrics using the Random Forest classifier.

Comparison of the effects of SMOTE algorithm processing and data processing without SMOTE algorithm.

Online searches through Web of Science and PubMed were conducted on 15 September, 2023 for articles published after 1950 using the following terms: TS = (ultra high dose rate OR ultra-high dose rate OR ultrahigh dose rate) AND TS = (in vivo OR animal model OR mice OR preclinical). The queries produced 980 results in total, with 564 results left after removing duplicate entries.The titles and abstracts were reviewed manually by two authors and the full-text of suitable manuscripts was further screened considering the factors such as topics, experiment condition and methods, research objects, endpoints, etc. The detailed record identification and screening flows based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) are summarized in Figure 1. Finally, forty articles were included in our analysis.The FLASH effect was confirmed if there were significant differences in experimental phenomena and data under the two radiation conditions. In the same article, the research items with different endpoints but otherwise identical conditions were regarded as one item. As summarized in Table 1, a total of 131 items were extracted from the 40 articles included in the analysis. For each item, the FLASH effect (1 represents significant sparing effect and 0 represents no sparing effect) and detailed parameters were recorded, including type and energy of the radiation, dose, dose rate, experimental object, pulse characteristics (if provided), etc.According to emulate the quantitative analyses of normal tissue effect in the clinic (QUANTEC), the probability of triggering the FLASH effect as a function of mean dose rate or dose was analyzed with the binary logistic regression model. The analysis was done using the SPSS software. For the statistical data items, there are large imbalances in the number of data entries with and without FLASH effect (people are more inclined to report the research with positive results). Therefore, a more balanced dataset was obtained by oversampling using the K-Means SMOTE algorithm (Figure S1), which was implemented using Python based on the imblearn library.The ROC curve (receiver operating characteristic curve) was plotted as FPR (False Positive Rate) against TPR (True Positive Rate) at different threshold values. The classification model was validated using the AUC (area under ROC curve) value, which was threshold and scale invariant.

This dataset is derived from the public dataset of road collisions that occurred in Montreal, which is accessible at https://www.donneesquebec.ca/recherche/dataset/vmtl-collisions-routieres. Unlike the original dataset, this dataset has been preprocessed (handling of missing data, data rebalancing via the SMOTE-ENN algorithm, etc.), and categorical variables have been encoded, making it ready for machine learning and other tasks. The .pkl file containing the encoding and the notebook demonstrating how to use the .pkl file are provided. For more details, please refer to the table below, which represents the data dictionary of this dataset. This dataset is shared under the Attribution License (CC-BY 4.0).

If you use this dataset for publication, please cite the following reference: Muktar, B.; Fono, V. Toward Safer Roads: Predicting the Severity of Traffic Accidents in Montreal Using Machine Learning. Electronics 2024, 13, 3036. https://doi.org/10.3390/electronics13153036

Background: Burnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physicians, nurses, teachers). The consequences of burnout involve decreased motivation, productivity, and overall diminished well-being. The machine learning-based prediction of burnout has therefore become the focus of recent research. In this study, the aim was to detect burnout using machine learning and to identify its most important predictors in a sample of Hungarian high-school teachers. Methods: The final sample consisted of 1,576 high-school teachers (522 male), who completed a survey including various sociodemographic and health-related questions and psychological questionnaires. Specifically, depression, insomnia, internet habits (e.g. when and why one uses the internet) and problematic internet usage were among the most important predictors tested in this study. Supervised classification algorithms were trained to detect burnout assessed by two well-known burnout questionnaires. Feature selection was conducted using recursive feature elimination. Hyperparameters were tuned via grid search with 5-fold cross-validation. Due to class imbalance, class weights (i.e. cost-sensitive learning), downsampling and a hybrid method (SMOTE-ENN) were applied in separate analyses. The final model evaluation was carried out on a previously unseen holdout test sample. Results: Burnout was detected in 19.7% of the teachers included in the final dataset. The best predictive performance on the holdout test sample was achieved by support vector machine with SMOTE-ENN (AUC = .942; balanced accuracy = .868, sensitivity = .898; specificity = .837). The best predictors of burnout were Beck’s Depression Inventory scores, Athen’s Insomnia Scale scores, subscales of the Problematic Internet Use Questionnaire and self-reported current health status. Conclusions: The performances of the algorithms were comparable with previous studies; however, it is important to note that we tested our models on previously unseen holdout samples suggesting higher levels of generalizability. Another remarkable finding is that besides depression and insomnia, other variables such as problematic internet use and time spent online also turned out to be important predictors of burnout.

Recent studies have shown that predictive models can supplement or provide alternatives to E. coli-testing for assessing the potential presence of food safety hazards in water used for produce production. However, these studies used balanced training data and focused on enteric pathogens. As such, research is needed to determine 1) if predictive models can be used to assess Listeria contamination of agricultural water, and 2) how resampling (to deal with imbalanced data) affects performance of these models. To address these knowledge gaps, this study developed models that predict nonpathogenic Listeria spp. (excluding L. monocytogenes) and L. monocytogenes presence in agricultural water using various combinations of learner (e.g., random forest, regression), feature type, and resampling method (none, oversampling, SMOTE). Four feature types were used in model training: microbial, physicochemical, spatial, and weather. “Full models” were trained using all four feature types, while “nested models” used between one and three types. In total, 45 full (15 learners*3 resampling approaches) and 108 nested (5 learners*9 feature sets*3 resampling approaches) models were trained per outcome. Model performance was compared against baseline models where E. coli concentration was the sole predictor. Overall, the machine learning models outperformed the baseline E. coli models, with random forests outperforming models built using other learners (e.g., rule-based learners). Resampling produced more accurate models than not resampling, with SMOTE models outperforming, on average, oversampling models. Regardless of resampling method, spatial and physicochemical water quality features drove accurate predictions for the nonpathogenic Listeria spp. and L. monocytogenes models, respectively. Overall, these findings 1) illustrate the need for alternatives to existing E. coli-based monitoring programs for assessing agricultural water for the presence of potential food safety hazards, and 2) suggest that predictive models may be one such alternative. Moreover, these findings provide a conceptual framework for how such models can be developed in the future with the ultimate aim of developing models that can be integrated into on-farm risk management programs. For example, future studies should consider using random forest learners, SMOTE resampling, and spatial features to develop models to predict the presence of foodborne pathogens, such as L. monocytogenes, in agricultural water when the training data is imbalanced.

The positive group of 608 signaling protein sequences was downloaded as FASTA format from Protein Databank (Berman et al., 2000) by using the “Molecular Function Browser” in the “Advanced Search Interface” (“Signaling (GO ID23052)”, protein identity cut-off = 30%). The negative group of 2077 non-signaling proteins was downloaded as the PISCES CulledPDB (http://dunbrack.fccc.edu/PISCES.php) (Wang & R. L. Dunbrack, 2003) (November 19th, 2012) using identity (degree of correspondence between two sequences) less than 20%, resolution of 1.6 Å and R-factor 0.25. The full dataset is containing 2685 FASTA sequences of protein chains from the PDB databank: 608 are signaling proteins and 2077 are non-signaling peptides. This kind of unbalanced data is not the most suitable to be used as an input for learning algorithms because the results would present a high sensitivity and low specificity; learning algorithms would tend to classify most of samples as part of the most common group. To avoid this situation, a pre-processing stage is needed in order to get a more balanced dataset, in this case by means of the synthetic minority oversampling technique (SMOTE). In short, SMOTE provides a more balanced dataset using an expansion of the lower class by creating new samples, interpolating other minority-class samples. After this pre-processing, the final dataset is composed of 1824 positive samples (signaling protein chains) and 2432 negative cases (non-signaling protein chains). Paper is available at: http://dx.doi.org/10.1016/j.jtbi.2015.07.038

Please cite: Carlos Fernandez-Lozano, Rubén F. Cuiñas, José A. Seoane, Enrique Fernández-Blanco, Julian Dorado, Cristian R. Munteanu, Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models, Journal of Theoretical Biology, Volume 384, 7 November 2015, Pages 50-58, ISSN 0022-5193, http://dx.doi.org/10.1016/j.jtbi.2015.07.038.(http://www.sciencedirect.com/science/article/pii/S0022519315003999)

Automated species identification and delimitation is challenging, particularly in rare and thus often scarcely sampled species, which do not allow sufficient discrimination of infraspecific versus interspecific variation. Typical problems arising from either low or exaggerated interspecific morphological differentiation are best met by automated methods of machine learning that learn efficient and effective species identification from training samples. However, limited infraspecific sampling remains a key challenge also in machine learning.

In this study, we assessed whether a data augmentation approach may help to overcome the problem of scarce training data in automated visual species identification. The stepwise augmentation of data comprised image rotation as well as visual and virtual augmentation. The visual data augmentation applies classic approaches of data augmentation and generation of artificial images using a Generative Adversarial Networks (GAN) approach. Descriptive feature vectors are derived from bottleneck features of a VGG-16 convolutional neural network (CNN) that are then stepwise reduced in dimensionality using Global Average Pooling and PCA to prevent overfitting. Finally, data augmentation employs synthetic additional sampling in feature space by an oversampling algorithm in vector space (SMOTE). Applied on four different image datasets, which include scarab beetle genitalia (Pleophylla, Schizonycha) as well as wing patterns of bees (Osmia) and cattleheart butterflies (Parides), our augmentation approach outperformed a deep learning baseline approach by means of resulting identification accuracy with non-augmented data as well as a traditional 2D morphometric approach (Procrustes analysis of scarab beetle genitalia).

Results of bioassay 439 dataset in experiment 1.

Data Sources: Training Set of Give me Some Credit Data in Kaggle Platform and a series of new data obtained after a series of pre-processing; Data format: three CSV copies; Data Description: The data set includes the age, income, family and loan situation of borrowers and there are 11 variables in total in which Serious Dlqin2yrs is lategory label,.1 represents default, 0 represents non-default, and another 10. Three variables are predictive characteristics. Smote_standardized_data is the result of traindata's basic processing, KNN filling missing values, outlier processing, standardization and balancing with SMOTE algorithm.

Average values of Accuracy, Kappa and imbalance ratio (min/maj) for the two methods in experiment 2.

Results of Bioassay 687 dataset in experiment 2.

Results of bioassay 721 dataset in experiment 1.

Results of bioassay 362 dataset in experiment 1.

Results of Bioassay 373 dataset in experiment 2.

Results of Bioassay 1608 dataset in experiment 2.

Results of bioassay 1284 dataset in experiment 1.