Paper proposes a DAE algorithm to improve credit card fraud detection.
problem Imbalanced data classification problem in credit card fraud detection.
method Proposes a denoising autoencoder neural network (DAE) algorithm to oversample and denoise minority class samples.
result Improves classification accuracy of minority class samples in imbalanced datasets.
GenSample uses genetic algorithms to improve minority class classification in imbalanced datasets.
problem Poor classification performance on minority class in imbalanced datasets.
method GenSample uses genetic algorithms to oversample minority class, considering difficulty and performance improvement.
result GenSample achieved the highest F-Score on 8 out of 9 real-world imbalanced datasets.
WOTBoost improves minority class accuracy in imbalanced datasets.
problem Imbalanced datasets lead to low accuracy in minority class classification.
method Combines weighted oversampling and boosting techniques.
result WOTBoost achieves best G mean and highest AUC score on multiple datasets.
Insider threat detection is getting an increased concern from academia, industry, and governments due to the growing number of malicious insider incidents. The existing approaches proposed for detecting insider threats still have a common shortcoming, which is the high number of false alarms (false positives). The chal…
A new sampling method balances imbalanced data using gamma distribution.
problem Imbalanced class distribution in data causes bias in classification algorithms.
method Intelligent resampling of minority class instances via gamma distribution.
result The proposed method outperforms existing techniques on 12 out of 24 datasets.
Enhanced loss function boosts fraud detection in auto insurance claims.
problem Class imbalance in auto insurance fraud detection.
method Structured three-stage training framework integrating convex surrogate, non-convex intermediate, and standard focal loss.
result Improves minority-class F1-scores and AUC compared to baseline methods.
Computational analysis on physiological signals would provide immense impact for enabling automated clinical analytics. However, the class imbalance issue where negative or minority class instances are rare in number impairs the robustness of the practical solution. The key idea of our approach is intelligent augmentat…
Model for detecting rare labels in imbalanced crowdsourcing data.
problem Detecting rare labels in imbalanced crowdsourcing data.
method Generative aggregation model combining item difficulty and class-dependent annotator competence.
result Our model achieves the highest minority recall while maintaining competitive balanced accuracy.
Paper proposes using truncated normal distribution for RRC model, improving detection of minority classes.
problem Improving weak classifiers in RRC models.
method Proposes using truncated normal distribution and soft confusion matrix for RRC model.
result Truncated-normal-based SCM algorithm outperforms beta distribution in discovering minority classes.
In many real-world binary classification tasks (e.g. detection of certain objects from images), an available dataset is imbalanced, i.e., it has much less representatives of a one class (a minor class), than of another. Generally, accurate prediction of the minor class is crucial but it's hard to achieve since there is…
Enhances Random Forest for imbalanced functional data classification.
problem Challenges in classifying imbalanced functional data.
method Functional Random Forest with Adaptive Cost-Sensitive Splitting (FRF-ACS).
result Significantly improves minority class recall and predictive performance.
Framework learns to transform majority to minority samples for balanced classification.
problem Imbalanced classification leading to biased predictions.
method Minimizes MMD and uses triplet loss for global alignment and boundary awareness.
result Consistent improvements over classical and generative baselines in AUROC, G-mean, F1-score, and MCC.
Cyber security has grown up to be a hot issue in recent years. How to identify potential malware becomes a challenging task. To tackle this challenge, we adopt deep learning approaches and perform flow detection on real data. However, real data often encounters an issue of imbalanced data distribution which will lead t…
This paper explores using KDE for balanced sampling in imbalanced datasets.
problem Imbalanced class distribution in data science.
method Kernel density estimation (KDE) for resampling the minority class.
result KDE-based resampling outperforms other techniques in F1-score and G-mean.
Robust cancer screening model using pre-trained ensembles for biomarkers.
problem Detecting early-stage cancer, especially in hard-to-diagnose cases like pancreatic cancer.
method Meta-trained Hyperfast model for robust classification, combined with ensembling of XGBoost and LightGBM.
result Achieved highest AUC of 0.9929 and robust performance on imbalanced datasets.
A new oversampling framework generates minority samples by perturbing majority classes.
problem Oversampling in imbalanced classification often neglects majority classes, leading to samples spread across the minority space.
method Introduces a counterfactual objective to generate new minority samples by perturbing majority samples.
result Generated minority samples are near the decision boundary and significantly outperform state-of-the-art methods.
robROSE tackles imbalanced fraud data by creating synthetic samples and detecting outliers.
problem Detecting fraud in imbalanced data sets where fraud is a minority class.
method Combines oversampling techniques with robust statistics to handle anomalies.
result robROSE enhances fraud detection while ignoring anomalies.
The paper tackles imbalanced classification under operational constraints, proposing a framework to maximize sensitivity.
problem Detecting minority class observations under severe class imbalance and operational constraints.
method Formal classification framework under capacity constraints, maximizing sensitivity while respecting a user-defined label limit.
result The optimal classifier under capacity constraints is equivalent to the Bayes classifier with reweighted prior probabilities.
Class imbalance problems manifest in domains such as financial fraud detection or network intrusion analysis, where the prevalence of one class is much higher than another. Typically, practitioners are more interested in predicting the minority class than the majority class as the minority class may carry a higher misc…
PCA is often used in anomaly detection and statistical process control tasks. For bivariate data, we prove that the minor projection (the least varying projection) of the PCA-rotated data is the most sensitive to distributional changes, where sensitivity is defined by the Hellinger distance between distributions before…
Data-driven fault diagnostics and prognostics suffers from class-imbalance problem in industrial systems and it raises challenges to common machine learning algorithms as it becomes difficult to learn the features of the minority class samples. Synthetic oversampling methods are commonly used to tackle these problems b…
M2m method improves deep learning performance on class-imbalanced datasets.
problem Class imbalance in labeled training datasets causes deep neural networks to generalize poorly to minority classes.
method Augment less-frequent classes by translating samples from more-frequent classes.
result Significantly improves generalization on minority classes compared to existing methods.
Mitigates anomaly score imbalance in long-tailed distributions.
problem Class imbalance in normal data leads to skewed anomaly detection performance.
method Proposes an importance-weighted loss function to balance anomaly scores.
result Improves anomaly detection performance by 0.043 on real-world datasets.
Study improves detection of cryptocurrency pump-and-dump schemes.
problem Class imbalance in P&D detection due to rare events.
method Synthetic Minority Oversampling Technique (SMOTE) and ensemble learning models.
result XGBoost and LightGBM achieved high recall rates (94.87% and 93.59%) with strong F1-scores.
AGGAN uses genetic algorithm with simulated annealing to generate minority class data.
problem Overcoming class imbalance in minority class data.
method AGGAN combines genetic algorithm and simulated annealing to train GANs on scarce minority class data.
result AGGAN effectively generates minority class data distributions from limited samples.
Improves A/B testing by detecting minor treatment effects.
problem Challenges in identifying small average treatment effects.
method Maximum probability-driven two-armed bandit (TAB) process with weighted mean volatility statistic.
result Significant improvement in A/B testing with reduced experimental costs.
Cardiovascular diseases are one of the most common causes of death in the world. Prevention, knowledge of previous cases in the family, and early detection is the best strategy to reduce this fact. Different machine learning approaches to automatic diagnostic are being proposed to this task. As in most health problems,…
LoRAS improves model performance on imbalanced datasets by better oversampling the minority class.
problem Imbalanced datasets lead to poor model performance, especially for the majority class.
method Localized Random Affine Shadowsampling (LoRAS) to oversample minority class data.
result LoRAS generates better ML models in terms of F1-Score and Balanced accuracy compared to SMOTE and its extensions.
Machine Learning has been steadily gaining traction for its use in Anomaly-based Network Intrusion Detection Systems (A-NIDS). Research into this domain is frequently performed using the KDD~CUP~99 dataset as a benchmark. Several studies question its usability while constructing a contemporary NIDS, due to the skewed r…
Decision trees can be biased towards minority class, contrary to belief.
problem Bias in decision trees towards minority class in imbalanced datasets.
method Critical evaluation of past literature, specific conditions analysis, tree-fitting adjustments, and post-hoc calibration methods.
result Decision trees can be biased towards minority class under specific conditions, not always towards majority.
Paper tackles spam filtering on forums using synthetic oversampling.
problem Imbalanced data in forums leads to poor spam detection.
method Synthetic Minority Over-sampling Technique (SMOTE) to balance data.
result Models trained with SMOTE outperform those trained on imbalanced data.
Capsule networks improve anomaly detection in high-dimensional datasets.
problem Anomaly detection in high-dimensional, class-imbalanced datasets.
method Used a capsule network architecture with autoencoder pre-training and dynamic routing.
result Capsule network outperformed other models in anomaly detection.
The paper extends logistic regression for unbounded majority classes and derives asymptotic properties.
problem Infinitely imbalanced logistic regression inference.
method Derive a second order expansion for slope parameter under unbounded majority class.
result The second order term converges to a normal distribution with a variance depending only on the minority class's mean.
Paper introduces a new optimization method for imbalanced datasets.
problem Overfitting in imbalanced datasets, especially in financial fraud detection.
method Fractional Weyl Integral optimization algorithm.
result Significantly improved performance in financial fraud detection (40% improvement in PR-AUC).
The paper addresses over-fitting in deep learning models trained on imbalanced data.
problem Over-fitting to minor classes in deep learning models trained on imbalanced data.
method Investigated feature deviation and proposed class-dependent temperatures (CDT) to compensate for it.
result CDT helps in overcoming feature deviation, improving model performance on test data of minor classes.
Conformal prediction fails to cover minority classes in imbalanced datasets, but a class-conditional fix improves coverage.
problem Conformal prediction fails to cover minority classes in imbalanced datasets, leading to poor performance on rare labels.
method Class-conditional conformal prediction to improve coverage of minority classes.
result Class-conditional conformal prediction restores minority coverage to target with a modest increase in prediction-set size.
A learning classifier must outperform a trivial solution, in case of imbalanced data, this condition usually does not hold true. To overcome this problem, we propose a novel data level resampling method - Clustering Based Oversampling for improved learning from class imbalanced datasets. The essential idea behind the p…
FCDD improves image anomaly detection without post-hoc explainers.
problem Image anomaly detection, especially pixel-wise.
method Fully Convolutional Data Description (FCDD) directly addresses anomaly detection without post-hoc methods.
result FCDD achieves state-of-the-art results on pixel-wise AD tasks.
Graphs with fat minors have a limited large-scale structure.
problem Understanding the large-scale structure of graphs excluding certain minors.
method Introduced the concept of Baker-treewidth and used it to prove asymptotic dimension bounds.
result Every hereditary class of bounded-degree graphs excluding some graph as a fat minor has asymptotic dimension at most 2.
Generative Adversarial Network model for class-imbalanced tabular data.
problem Class imbalance in binary classification problems.
method Generative Adversarial Network (GAN) with synthetic minority class samples.
result Improves average precision compared to re-weighting and oversampling techniques.
MixBoost generates synthetic instances to balance imbalanced datasets.
problem Training models on imbalanced datasets.
method Iterative data augmentation method that selects and combines instances from majority and minority classes.
result MixBoost outperforms existing approaches on 20 benchmark datasets.
Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. Howev…
Price dynamics is analyzed in terms of a model which includes the possibility of effective forces due to trend followers or trend adverse strategies. The method is tested on the data of a minority-majority model and indeed it is capable of reconstructing the prevailing traders' strategies in a given time interval. Then…
Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers. In this work we propose balancing GAN (BAGAN) as an augmentation tool to restore balance in imbalanced datasets. This is challenging because the few minority-class images may not be enou…
Threshold tests have recently been proposed as a useful method for detecting bias in lending, hiring, and policing decisions. For example, in the case of credit extensions, these tests aim to estimate the bar for granting loans to white and minority applicants, with a higher inferred threshold for minorities indicative…
New model explains neural collapse and limits on minority classes in imbalanced datasets.
problem Understanding and predicting performance limits of deep learning models on imbalanced datasets.
method Layer-Peeled Model, a nonconvex optimization program isolating top layers and applying constraints.
result Reveals a new phenomenon called Minority Collapse that limits deep learning models on minority classes.
RaRecognize learns to recognize rare classes in a stream of data.
problem Learning to recognize rare classes in a continuous stream of data.
method Estimates a general decision boundary, learns individual rare subclasses, flags new subclasses.
result RaRecognize outperforms state-of-the-art baselines on real-world datasets.
A novel resampling technique addresses class imbalance in imbalanced datasets.
problem Class imbalance in real-world datasets, especially in rare event detection.
method Developed two oversampling algorithms: G1Nos 1-Nearest Neighbour.
result Our oversampling algorithms outperform state-of-the-art methods in all metrics.