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.
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…
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 …
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…
Paper reviews and compares methods for handling imbalanced data.
Study shows imbalanced labels can be beneficial but not always in class-imbalanced learning.
A new method for handling imbalanced data in regression models.
Study challenges the necessity of data augmentation for improving predictions on imbalanced text datasets.
Paper introduces a new method to improve learning on imbalanced regression problems.
SkewPNN uses probabilistic neural networks with skew-normal kernels to improve classification of imbalanced data.
Proposes a new data augmentation method for imbalanced datasets in both classification and regression.
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…
PET-TURTLE improves clustering accuracy for imbalanced data.
Study investigates how preprocessing, feature selection, and model selection affect performance on imbalanced genetic data.
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…
Paper uses virtual big data to improve autoencoder training and address imbalanced data classification.
The paper corrects bias in synthetic data for imbalanced learning.
Improves comparison of F-measures for imbalanced datasets.
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…
New method improves safety analytics by addressing imbalanced data issues.
This paper tackles imbalanced data in binary classification problems.
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…
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…
New framework for learning from imbalanced data with theoretical guarantees.
Study tackles imbalanced data in car insurance claims prediction.
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 …
Proposes GMOTE for better handling imbalanced data.
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…
Optimal downsampling improves GLM performance in imbalanced classification.
EKM addresses imbalanced data clustering by repelling centroids in large clusters.
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…
Proposes a method to balance imbalanced image datasets using capsule-GAN.
Solves biased pseudo-labels in imbalanced SSL by refining them.
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…
A new algorithm improves credit scoring accuracy for imbalanced data.
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…
UBMF tackles fault diagnosis in imbalanced industrial data with enhanced accuracy and adaptability.
LDAO addresses imbalanced regression by learning local distribution structures.
We propose a novel unsupervised generative model that learns to disentangle object identity from other low-level aspects in class-imbalanced data. We first investigate the issues surrounding the assumptions about uniformity made by InfoGAN, and demonstrate its ineffectiveness to properly disentangle object identity in …
A new sampling method balances imbalanced data using gamma distribution.
Differences in data size per class, also known as imbalanced data distribution, have become a common problem affecting data quality. Big Data scenarios pose a new challenge to traditional imbalanced classification algorithms, since they are not prepared to work with such amount of data. Split data strategies and lack o…
Unified framework for imbalanced data resampling improves classification performance.
PROTOCOL tackles imbalanced multi-view clustering by enhancing contrastive learning.
Spectral clustering is sensitive to how graphs are constructed from data particularly when proximal and imbalanced clusters are present. We show that Ratio-Cut (RCut) or normalized cut (NCut) objectives are not tailored to imbalanced data since they tend to emphasize cut sizes over cut values. We propose a graph partit…
Paper proposes a new autoencoder metric for balanced learning in imbalanced tabular datasets.
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…
GAT-RWOS uses graph attention to improve imbalanced data classification.