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.
A new oversampling framework generates minority samples by perturbing majority classes.
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.
AGGAN uses genetic algorithm with simulated annealing to generate minority class data.
Imbalanced data classification problem has always been a popular topic in the field of machine learning research. In order to balance the samples between majority and minority class. Oversampling algorithm is used to synthesize new minority class samples, but it could bring in noise. Pointing to the noise problems, thi…
Decision trees can be biased towards minority class, contrary to belief.
Classifiers trained with class-imbalanced data are known to perform poorly on test data of the "minor" classes, of which we have insufficient training data. In this paper, we investigate learning a ConvNet classifier under such a scenario. We found that a ConvNet significantly over-fits the minor classes, which is quit…
The paper extends logistic regression for unbounded majority classes and derives asymptotic properties.
A new sampling method balances imbalanced data using gamma distribution.
Conformal prediction fails to cover minority classes in imbalanced datasets, but a class-conditional fix improves coverage.
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…
Graphs with fat minors have a limited large-scale structure.
Generative Adversarial Network model for class-imbalanced tabular data.
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…
The Synthetic Minority Oversampling TEchnique (SMOTE) is widely-used for the analysis of imbalanced datasets. It is known that SMOTE frequently over-generalizes the minority class, leading to misclassifications for the majority class, and effecting the overall balance of the model. In this article, we present an approa…
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…
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…
New model explains neural collapse and limits on minority classes in imbalanced datasets.
Framework learns to transform majority to minority samples for balanced classification.
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…
Machine learning classifiers often stumble over imbalanced datasets where classes are not equally represented. This inherent bias towards the majority class may result in low accuracy in labeling minority class. Imbalanced learning is prevalent in many real-world applications, such as medical research, network intrusio…
The paper is partially withdrawn: in its current form, Lemma 2.3 is false, so that our proof of Theorem A and Proposition B has an important gap. We were unable to fix it yet. Any help is most welcome. We prove that the restriction of surface minority to fiber surfaces of divides is a well-quasi-order. Here surface min…
Study embeddability of 2-complexes in 4-space, proving Heawood family's excluded minors.
Paper studies asymptotic dimension and Assouad-Nagata dimension of graphs and surfaces.
CBDA improves active learning for semantic segmentation, especially with imbalanced classes.
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…
CopulaSMOTE addresses class imbalance in diabetes prediction models.
A new CA-GAN architecture improves minority class data generation in health datasets.
New invariant links graph structure to tropical curve properties.
Bayesian framework improves minority class performance in class-imbalanced data.
Enhanced loss function boosts fraud detection in auto insurance claims.
Graphs on surfaces have a 2-dimensional large scale structure.
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 algorithm enhances minority class representation in imbalanced datasets.
Robust cancer screening model using pre-trained ensembles for biomarkers.
The paper explores how deep generative models can improve performance on imbalanced datasets.
Well-quasi-orders proved on embedded planar graphs.
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…
Study shows quasipositive fiber surfaces can't be well-ordered.
In the area of credit risk analytics, current Bankruptcy Prediction Models (BPMs) struggle with (a) the availability of comprehensive and real-world data sets and (b) the presence of extreme class imbalance in the data (i.e., very few samples for the minority class) that degrades the performance of the prediction model…
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…
Given a labeled dataset that contains a rare (or minority) class of of-interest instances, as well as a large class of instances that are not of interest, how can we learn to recognize future of-interest instances over a continuous stream? We introduce RaRecognize, which (i) estimates a general decision boundary betwee…
Analysis shows data imbalance slows learning curves for minority and majority classes.
MCRAGE generates synthetic data to balance healthcare datasets.
DSAEE FS selects features for imbalanced data.
In this paper we consider minors of ribbon graphs (or, equivalently, cellularly embedded graphs). The theory of minors of ribbon graphs differs from that of graphs in that contracting loops is necessary and doing this can create additional vertices and components. Thus the ribbon graph minor relation is incompatible wi…
Theoretical and empirical study on SMOTE rebalancing strategy for imbalanced data.
A novel minimax classifier tackles imbalanced datasets with few minority samples.