Summary table: Oversampling techniques using SMOTE, ADASYN, and weighted rare classes.

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.

Number of instances increased by SMOTE technique.

Dataset

This dataset was created by Anand Philip

Released under CC0: Public Domain

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Dataset

This dataset was created by Elvin Aghammadzada

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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.

SMOTE: synthetic minority over-sampling technique.

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:

This is the dataset from our research. A research paper has already been published and can be accessed at https://www.sciencedirect.com/science/article/pii/S2588914124000169.

Performance of machine learning models using SMOTE-balanced dataset.

Dataset

This dataset was created by Rafael Novello

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This dataset contains more than 17000 data of credit card holder with 20 predictor variables and 1 binary target variable. The corresponding R code for comparing several proposed (density-based) and existing synthetic oversampling methods (SMOTE-based) is also provided.

Chinese

Dataset

This dataset was created by Alexis Moraga

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Classification result classifiers using TF-IDF with SMOTE.

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This repository contains the data and associated results of all experiments conducted in our work "Signature Informed Sampling for Transcriptomic Data". In this work we propose a simple, novel, non-parametric method for augmenting data inspired by the concept of chromosomal crossover. We benchmark our proposed methods against random oversampling, SMOTE, modified versions of gamma-Poisson and Poisson sapling, and the unbalanced data.

The compressed file data_5x5stratified.zip contains all the data used for our experiments. This includes the original count data based off of which augmentation was performed, the cross validation split indices as a json file, the training and validation data (TCGA) augmented by the various augmentation methods mentioned in our study, a test set (containing only real samples from TCGA) and an external test set (CPTAC) standardised accordingly with respect to each augmentation method and training data per cv split.

The compressed file 5x5_Results.zip contains all the results from all the experiments. This includes the parameter files used to train the various models, the metrics computed, the latent space of train, validation and test (if the model is a VAE), and the trained model itself for all 25 (5x5) splits.

The diagnosis of human knee abnormalities using the surface electromyography (sEMG) signal obtained from lower limb muscles with machine learning is a major problem due to the noisy nature of the sEMG signal and the imbalance in data corresponding to healthy and knee abnormal subjects. To address this challenge, a combination of wavelet decomposition (WD) with ensemble empirical mode decomposition (EEMD) and the Synthetic Minority Oversampling Technique (S-WD-EEMD) is proposed. In this study, a hybrid WD-EEMD is considered for the minimization of noises produced in the sEMG signal during the collection, while the Synthetic Minority Oversampling Technique (SMOTE) is considered to balance the data by increasing the minority class samples during the training of machine learning techniques. The findings indicate that the hybrid WD-EEMD with SMOTE oversampling technique enhances the efficacy of the examined classifiers when employed on the imbalanced sEMG data. The F-Score of the Extra Tree Classifier, when utilizing WD-EEMD signal processing with SMOTE oversampling, is 98.4%, whereas, without the SMOTE oversampling technique, it is 95.1%.

Diabetes prediction is an ongoing study topic in which medical specialists are attempting to forecast the condition with greater precision. Diabetes typically stays lethargic, and on the off chance that patients are determined to have another illness, like harm to the kidney vessels, issues with the retina of the eye, or a heart issue, it can cause metabolic problems and various complexities in the body. Various worldwide learning procedures, including casting a ballot, supporting, and sacking, have been applied in this review. The Engineered Minority Oversampling Procedure (Destroyed), along with the K-overlay cross-approval approach, was utilized to achieve class evening out and approve the discoveries. Pima Indian Diabetes (PID) dataset is accumulated from the UCI Machine Learning (UCI ML) store for this review, and this dataset was picked. A highlighted engineering technique was used to calculate the influence of lifestyle factors. A two-phase classification model has been developed to predict insulin resistance using the Sequential Minimal Optimisation (SMO) and SMOTE approaches together. The SMOTE technique is used to preprocess data in the model’s first phase, while SMO classes are used in the second phase. All other categorization techniques were outperformed by bagging decision trees in terms of Misclassification Error rate, Accuracy, Specificity, Precision, Recall, F1 measures, and ROC curve. The model was created using a combined SMOTE and SMO strategy, which achieved 99.07% correction with 0.1 ms of runtime. The suggested system’s result is to enhance the classifier’s performance in spotting illness early.