A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
INGB improves oversampling for noisy imbalanced datasets.
Framework tackles class imbalance and noisy labels in active learning.
New method tackles label noise on imbalanced datasets by considering class-specific uncertainty.
ART adapts class-wise resampling to improve imbalanced classification performance.
INN method refines clean labeled data from noisy labels.
HardVis helps visually manage imbalanced data by sampling hard instances.
VIR model improves regression accuracy and uncertainty estimation for imbalanced data.
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.
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.
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.
Meta Additive Model learns auto-weighting for robust sparse learning.
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.
Male infertility is a disease which affects approximately 7% of men. Sperm morphology analysis (SMA) is one of the main diagnosis methods for this problem. Manual SMA is an inexact, subjective, non-reproducible, and hard to teach process. As a result, in this paper, we introduce a novel automatic SMA based on a neural …
Paper reviews and compares methods for handling imbalanced data.
Paper introduces a new method to improve learning on imbalanced regression problems.
Study challenges the necessity of data augmentation for improving predictions on imbalanced text datasets.
LDAO addresses imbalanced regression by learning local distribution structures.
SkewPNN uses probabilistic neural networks with skew-normal kernels to improve classification of imbalanced data.
Improves comparison of F-measures for imbalanced datasets.
A new method for handling imbalanced data in regression models.
Study investigates how preprocessing, feature selection, and model selection affect performance on imbalanced genetic data.
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.
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.
PET-TURTLE improves clustering accuracy for imbalanced data.
New framework for learning from imbalanced data with theoretical guarantees.
Study tackles imbalanced data in car insurance claims 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.
Solves biased pseudo-labels in imbalanced SSL by refining them.
A new asymmetric contrastive loss improves performance on imbalanced datasets.
M2m method improves deep learning performance on class-imbalanced datasets.
Semi-Supervised Learning (SSL) has achieved great success in overcoming the difficulties of labeling and making full use of unlabeled data. However, SSL has a limited assumption that the numbers of samples in different classes are balanced, and many SSL algorithms show lower performance for the datasets with the imbala…
In this study, classification problems based on feedforward neural networks in a data-imbalanced environment are considered. Learning from an imbalanced dataset is one of the most important practical problems in the field of machine learning. A weighted loss function (WLF) based on a cost-sensitive approach is a well-k…
Paper uses virtual big data to improve autoencoder training and address imbalanced data classification.
This paper tackles imbalanced data in binary classification problems.
Proposes PUUPL for PUL in imbalanced datasets, boosting minority class signals.