CP-GAN generates images selectively conditioned on class specificity, capturing between-class relationships.
problem Generating images selectively conditioned on class specificity in class-overlapping data.
method Proposed Classifier's Posterior GAN (CP-GAN) that redesigns generator input and objective function for class-overlapping data.
result Demonstrated effectiveness of CP-GAN using both controlled and real-world class-overlapping data.
Machine learning models predict bluebottles' presence on beaches, addressing class imbalance and unreliable absence data.
problem Predicting bluebottles' presence on beaches with machine learning, tackling class imbalance and unreliable absence data.
method Used Multilayer Perceptron, Random Forest, and XGBoost models; employed data augmentation techniques like SMOTE, Random Undersampling, and Synthetic Negative Approach.
result Random Forests combined with Synthetic Negative Approach provided the best predictive model, identifying wind direction as a key factor.
AdaDEM decouples EM into two parts to improve class overlap and uncertainty.
problem Improper EM limits its effectiveness in various machine learning tasks.
method Decouple EM into CADF and GMC, and AdaDEM normalizes CADF reward and uses MEC.
result AdaDEM outperforms classical EM and improves performance in noisy and dynamic environments.
New metrics assess class overlap and imbalance in datasets.
problem Class overlap and imbalance make datasets hard to classify.
method Developed new metrics based on ball coverage by classes.
result Metrics correlate well with classifier performance.
Efficiently models categorical data with low to medium class overlap, improving accuracy over standard distributions.
problem Poor parameter estimates and accuracy in multinomial and Dirichlet multinomial distributions when assumptions are violated.
method Introduces Beta-Liouville multinomial distribution and efficient estimation methods.
result Beta-Liouville multinomial outperforms standard distributions on two out of four datasets.
Temperature scaling fails for distributions with class overlaps, while Mixup improves calibration.
problem Temperature scaling's performance degrades with class overlaps, leading to poor calibration.
method Identified temperature scaling's limitations and compared it with Mixup for calibration.
result Mixup significantly outperforms temperature scaling in calibration metrics with class overlaps.
We find flat band Hamiltonians and Ginsparg-Wilson relations for symmetry classes.
problem Finding flat band Hamiltonians and Ginsparg-Wilson relations for symmetry classes.
method Integrating out the additional bulk direction to obtain effective Dirac operators and then deriving flat and overlap Dirac operators.
result Established Ginsparg-Wilson relations and mod-two index theorems for each symmetry class.
Proposes a meta-algorithm for classification with overlapping classes in high-energy physics.
problem Challenges of class overlap in binary classification.
method Combines bagging and boosting techniques with a randomization trick.
result Improves statistical significance of Higgs discovery.
Two classes of gamma-ray bursts (GRBs), short and long, have been determined without any doubts, and are usually ascribed to different progenitors, yet these classes overlap for a variety of descriptive parameters. A subsample of 46 long and 22 short Fermi GRBs with estimated Hurst Exponents (HEs), complemented by mi…
The problem of class imbalance along with class-overlapping has become a major issue in the domain of supervised learning. Most supervised learning algorithms assume equal cardinality of the classes under consideration while optimizing the cost function and this assumption does not hold true for imbalanced datasets whi…
A number of machine learning algorithms are using a metric, or a distance, in order to compare individuals. The Euclidean distance is usually employed, but it may be more efficient to learn a parametric distance such as Mahalanobis metric. Learning such a metric is a hot topic since more than ten years now, and a numbe…
A new framework boosts classifier performance on imbalanced data.
problem Learning from highly imbalanced massive datasets.
method Self-paced ensemble with under-sampling to harmonize data hardness.
result Robust performance even under overlapping classes and skewed distribution.
Semi-supervised learning benefits from informative missing labels, improving classifier performance.
problem Missing labels in semi-supervised learning can be informative, improving classifier performance.
method Formulates missingness as a mixture model problem and uses EM algorithm for fitting.
result Modelling informative missingness can yield a classifier with smaller expected error than a completely labelled sample.
Paper proposes DAPNA to improve few-shot learning with domain adaptation.
problem Improving recognition of unseen classes with limited samples.
method Domain adaptation prototypical network with attention (DAPNA).
result DAPNA outperforms state-of-the-art FSL alternatives in experiments.
CCCDs tackle class imbalance in classification.
problem Class imbalance in statistical classification.
method Class cover catch digraphs (CCCDs) for graph theoretic solutions.
result CCCD classifiers perform well in class imbalance scenarios.
FAE framework tackles fairness in machine learning by balancing data and adjusting decision boundaries.
problem Discrimination in automated decision-making based on machine learning algorithms.
method Combines pre- and post-processing fairness interventions to address group imbalance, class imbalance, and class overlap.
result Improves fairness in machine learning models by balancing data and adjusting decision boundaries.
This paper surveys various data balancing methods for imbalanced datasets.
problem Imbalanced datasets bias predictions and degrade classifier performance.
method Extensive review of oversampling, undersampling, adaptive, generative, combination, and ensemble methods.
result No single method universally outperforms others; selection depends on dataset characteristics.
Develops a data-driven fault diagnosis framework for time-series data.
problem Fault diagnosis of dynamic systems using imbalanced and unknown fault classes.
method Kullback-Leibler divergence, data-driven fault classification, open-set classification.
result Framework handles imbalanced datasets, class overlapping, and unknown faults.
Confident learning estimates label quality by identifying and correcting noisy data.
problem Label quality in datasets, especially in noisy environments.
method Combines principles of pruning, probabilistic thresholds, and ranking to estimate noise and uncorrupted labels.
result Generalized CL framework that provably finds label errors and improves model accuracy.
ALIEN improves uncertainty estimation of language models by refining entropy-based methods.
problem Overconfidence in uncertainty estimation for language models, especially for difficult inputs.
method ALIEN refines entropy-based uncertainty by aligning it with prediction reliability, using a lightweight uncertainty head.
result ALIEN consistently outperforms strong baselines in detecting incorrect predictions and achieving the lowest calibration error.
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.
New metric scores perturbations across populations, not cells, improving model comparison.
problem Single-cell perturbation data overlaps, making per-cell accuracy unreliable.
method Average per-cell probability vectors over all cells of a perturbation to form a population profile and rank candidate perturbations.
result Classifier Discrimination Score (CDS) identifies true perturbation more reliably than pseudobulk-based scores.
Dynamic Classifier Selection (DCS) techniques have difficulty in selecting the most competent classifier in a pool, even when its presence is assured. Since the DCS techniques rely only on local data to estimate a classifier's competence, the manner in which the pool is generated could affect the choice of the best cla…