The logistic regression model is known to converge to a Poisson point process model if the binary response tends to infinitely imbalanced. In this paper, it is shown that this phenomenon is universal in a wide class of link functions on binomial regression. The proof relies on the extreme value theory. For the logit, p…
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48 results for “infinitely imbalanced”
Asymptotic Normality of Infinite Centered Random Forests -Application to Imbalanced Classificationstat.ML
The paper studies a rebalanced dataset for imbalanced classification using Centered Random Forests.
problem Imbalanced classification where one class is underrepresented.
method Theoretical analysis of Centered Random Forests (CRF) with rebalanced datasets and debiasing techniques.
result Theoretical Central Limit Theorem (CLT) for the infinite CRF and debiased estimator IS-ICRF.
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 develops a new method for open-set and imbalanced classification with valid prediction sets.
problem Tackles open-set and imbalanced classification with new prediction methods.
method Develops a new family of conformal p-values and a selective sample splitting algorithm.
result Valid prediction sets with valid coverage in open-set scenarios and informative predictions under extreme class imbalance.
VIR model improves regression accuracy and uncertainty estimation for imbalanced data.
problem Imbalanced regression datasets lead to poor model accuracy and uncertainty estimation.
method VIR model uses probabilistic smoothing and reweighting to estimate latent representations and uncertainty.
result VIR model outperforms state-of-the-art models in accuracy and uncertainty estimation.
Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem. The problem becomes particularly acute when we consider incremental classifiers operating on imbalanced data streams, especially when the l…
Study shows imbalanced labels can be beneficial but not always in class-imbalanced learning.
problem Challenges in class-imbalanced learning with heavy label bias.
method Systematic investigation of semi-supervised and self-supervised approaches to leverage imbalanced labels.
result Imbalanced labels are valuable in semi-supervised learning but not always in self-supervised learning.
Private business schools in India face a common problem of selecting quality students for their MBA programs to achieve the desired placement percentage. Generally, such data sets are biased towards one class, i.e., imbalanced in nature. And learning from the imbalanced dataset is a difficult proposition. This paper pr…
Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data. We develop a latent variable model that can cope with imbalanced data by dividing the latent space into a shared space…
Optimal downsampling improves GLM performance in imbalanced classification.
problem Improving GLM performance in imbalanced classification.
method Proposed a pseudo maximum likelihood estimator for optimal downsampling.
result The introduced estimator outperforms existing alternatives in both synthetic and empirical data.
When the training data in a two-class classification problem is overwhelmed by one class, most classification techniques fail to correctly identify the data points belonging to the underrepresented class. We propose Similarity-based Imbalanced Classification (SBIC) that learns patterns in the training data based on an …
Proposes a new data augmentation method for imbalanced datasets in both classification and regression.
problem Imbalanced datasets in supervised learning, especially in regression.
method GOLIATH algorithm based on kernel density estimates for classification and regression.
result Significant improvement over existing state-of-the-art techniques in imbalanced regression.
Paper tackles class-imbalanced semi-supervised learning, improving performance.
problem Class imbalance in semi-supervised learning datasets.
method Proposes Suppressed Consistency Loss (SCL) to handle class imbalance.
result Method shows better performance in class-imbalanced semi-supervised learning environments.
A new BN method corrects size mismatch in WLF for imbalanced data.
problem Learning from imbalanced datasets in neural networks.
method Proposes weighted batch normalization (WBN) to correct size mismatch between BN and WLF.
result WBN corrects size mismatch and improves classification performance in imbalanced datasets.
This study is motivated by the magnitude of the problem of Louisiana high school dropout and its negative impacts on individual and public well-being. Our goal is to predict students who are at risk of high school dropout, by examining Louisiana administrative dataset. Due to the imbalanced nature of the dataset, imbal…
Mix-IRLS solves imbalanced mixed linear regression problems efficiently.
problem Imbalanced mixed linear regression problems.
method Sequential robust regression approach.
result Mix-IRLS outperforms other methods on imbalanced mixtures and real-world datasets.
Paper reviews and compares methods for handling imbalanced data.
problem Handling imbalanced data sets in financial industry.
method Reviewed and compared under-sampling/over-sampling methodologies.
result Performance analysis of class-imbalance methods, modeling algorithms, and grid search criteria.
Paper introduces a new method to improve learning on imbalanced regression problems.
problem Imbalanced distribution learning in predictive modeling reduces standard algorithms' performance.
method The paper proposes a novel method using disentangled VAEs and Smoothed Bootstrap in the latent space.
result The method improves learning on tabular data within the Imbalanced Regression framework.
Study challenges the necessity of data augmentation for improving predictions on imbalanced text datasets.
problem Improving predictions on imbalanced text datasets.
method Comparing classifier cutoff adjustments to data augmentation techniques.
result Classifier cutoff adjustments can produce similar results to data augmentation without the need for additional data.
LDAO addresses imbalanced regression by learning local distribution structures.
problem Imbalanced regression with sparse target regions difficult for models.
method LDAO learns local distribution structures, models and samples from each, then merges.
result LDAO outperforms state-of-the-art methods on 45 imbalanced datasets.
SkewPNN uses probabilistic neural networks with skew-normal kernels to improve classification of imbalanced data.
problem Imbalanced data distribution leading to biased predictions for minority classes.
method Probabilistic neural networks with skew-normal kernel function and Bat optimization algorithm for hyperparameter tuning.
result SkewPNN and BA-SkewPNN outperform other methods in both balanced and imbalanced datasets.
Improves comparison of F-measures for imbalanced datasets.
problem Comparing F-measures for classification algorithms on imbalanced data.
method Two improvements to existing F-measure comparison methods.
result Enhanced accuracy in comparing F-measures for classification.
A new method for handling imbalanced data in regression models.
problem Imbalanced data in regression models with continuous or discrete covariates.
method Combines weighted resampling and data augmentation procedures.
result Improves the accuracy of model estimates by addressing imbalanced data.
Study investigates how preprocessing, feature selection, and model selection affect performance on imbalanced genetic data.
problem Challenges in using machine learning on imbalanced genetic datasets.
method Comparative analysis of data preprocessing, feature selection techniques, and machine learning models on imbalanced genetic data.
result Class-imbalanced target variables and skewed predictors have little to no impact on classification performance.
Data in real-world application often exhibit skewed class distribution which poses an intense challenge for machine learning. Conventional classification algorithms are not effective in the case of imbalanced data distribution, and may fail when the data distribution is highly imbalanced. To address this issue, we prop…
Imbalanced datasets are ubiquitous. Classification performance on imbalanced datasets is generally poor for the minority class as the classifier cannot learn decision boundaries well. However, in sensitive applications like fraud detection, medical diagnosis, and spam identification, it is extremely important to classi…
PROTOCOL tackles imbalanced multi-view clustering by enhancing contrastive learning.
problem Class imbalance in real-world multi-view data.
method PROTOCOL uses partial optimal transport to perceive and mitigate imbalance, enhancing contrastive learning.
result PROTOCOL significantly improves clustering performance on imbalanced multi-view data.
This paper discusses financial fraud detection in imbalanced dataset using homogeneous and non-homogeneous Poisson processes. The probability of predicting fraud on the financial transaction is derived. Applying our methodology to the financial dataset shows a better predicting power than a baseline approach, especiall…
In this study, we consider classification problems based on neural networks in data-imbalanced environment. Learning from an imbalanced data set is one of the most important and practical problems in the field of machine learning. A weighted loss function based on cost-sensitive approach is a well-known effective metho…
New method improves safety analytics by addressing imbalanced data issues.
problem Imbalanced safety datasets lead to inaccurate predictions and management problems.
method Extended accident triangle theory and three oversampling methods.
result Robust improvements in machine learning algorithms for safety analytics.
PET-TURTLE improves clustering accuracy for imbalanced data.
problem Imbalanced data causes clustering errors.
method Generalizes cost function and introduces sparse logits.
result PET-TURTLE enhances overall clustering accuracy for imbalanced data.
Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Datacs.LG
New framework for learning from imbalanced data with theoretical guarantees.
problem Class imbalance in machine learning, especially in multi-class problems.
method Theoretical framework and new margin loss function for imbalanced classification.
result Proves strong -consistency of the proposed margin loss function.
Study tackles imbalanced data in car insurance claims prediction.
problem Predicting rare events (claims) in car insurance with imbalanced data.
method Various machine learning techniques (logistic-regression, decision tree, random forest, xgBoost, feed-forward network) applied to imbalanced dataset.
result Comparison of machine learning algorithms' performance in claim occurrence prediction.
Imbalanced Learning is an important learning algorithm for the classification models, which have enjoyed much popularity on many applications. Typically, imbalanced learning algorithms can be partitioned into two types, i.e., data level approaches and algorithm level approaches. In this paper, the focus is to develop a…
A natural way of handling imbalanced data is to attempt to equalise the class frequencies and train the classifier of choice on balanced data. For two-class imbalanced problems, the classification success is typically measured by the geometric mean (GM) of the true positive and true negative rates. Here we prove that G…
Class-imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers have been…
Class-imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers have been…
Two novel methods improve network embedding for completely-imbalanced labels.
problem Improving network embedding for networks with completely-imbalanced labels.
method Two novel semi-supervised network embedding methods: RSDNE and RECT.
result Experimental results show the superiority of the proposed methods.
Solves biased pseudo-labels in imbalanced SSL by refining them.
problem Imbalanced class distributions in semi-supervised learning lead to biased pseudo-labels.
method Formulates a convex optimization problem to refine pseudo-labels and develops an efficient algorithm, DARP.
result Demonstrates the effectiveness of DARP in various imbalanced semi-supervised scenarios.
A new method for handling imbalanced big data using ensembles and smart data.
problem Imbalanced data distribution in big data scenarios.
method Smart Data driven Decision Trees Ensemble (SD_DeTE) methodology.
result SD_DeTE outperforms Random Forest in handling imbalanced binary classification problems in big data.
A new asymmetric contrastive loss improves performance on imbalanced datasets.
problem Improving performance on imbalanced datasets using contrastive learning.
method Introducing an asymmetric contrastive loss (ACL) and asymmetric focal contrastive loss (AFCL).
result AFCL outperforms CL and FCL in terms of weighted and unweighted classification accuracies on imbalanced datasets.
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.
Paper uses virtual big data to improve autoencoder training and address imbalanced data classification.
problem Imbalanced data classification and autoencoder over-fitting.
method Cross-concatenation using Virtual Big Data.
result Cross-concatenation method effectively balances imbalanced class distributions.
This paper tackles imbalanced data in binary classification problems.
problem Imbalanced data leads to skewed results in classification problems.
method Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) Sampling Approach.
result Synthetic data points enhance understanding of oversampling techniques.
Proposes PUUPL for PUL in imbalanced datasets, boosting minority class signals.
problem Imbalanced datasets and model calibration in PUL.
method Uncertainty-aware pseudo-labeling procedure (PUUPL).
result Substantial performance gains in highly imbalanced settings.
Proposes a method to balance imbalanced image datasets using capsule-GAN.
problem Imbalanced datasets challenge deep learning techniques.
method Capsule-GAN, combining GANs and capsule networks, addresses imbalance by generating minority class samples.
result Improves learning from imbalanced data with fewer parameters.
The paper corrects bias in synthetic data for imbalanced learning.
problem Challenges in balancing false positive and negative rates in imbalanced data.
method Proposes a bias correction procedure to generate synthetic data for minority groups.
result Enhances prediction accuracy while avoiding overfitting.
Cavity Filling: Pseudo-Feature Generation for Multi-Class Imbalanced Data Problems in Deep Learningcs.LG
Herein, we generate pseudo-features based on the multivariate probability distributions obtained from the feature maps in layers of trained deep neural networks. Further, we augment the minor-class data based on these generated pseudo-features to overcome the imbalanced data problems. The proposed method, i.e., cavity …