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.
This paper tackles imbalanced classification with weakly supervised oversampling.
This paper uses LLMs to generate synthetic data to improve classification accuracy in imbalanced datasets.
INGB improves oversampling for noisy imbalanced datasets.
Proposes a new technique for handling imbalanced data.
This paper tackles imbalanced data in binary classification problems.
The paper analyzes SMOTE for imbalanced classification, providing theoretical bounds and guidelines.
Paper tackles imbalanced time series classification with a novel oversampling method.
Proposes GMOTE for better handling imbalanced data.
This chapter tackles class imbalance in datasets to promote data democracy.
A new LLM-based method enhances diversity in oversampling for imbalanced classification.
MixBoost generates synthetic instances to balance imbalanced datasets.
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…
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…
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…
GAT-RWOS uses graph attention to improve imbalanced data classification.
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…
We aim at developing and improving the imbalanced business risk modeling via jointly using proper evaluation criteria, resampling, cross-validation, classifier regularization, and ensembling techniques. Area Under the Receiver Operating Characteristic Curve (AUC of ROC) is used for model comparison based on 10-fold cro…
We explore several oversampling techniques for an imbalanced multi-label classification problem, a setting often encountered when developing models for Computer-Aided Diagnosis (CADx) systems. While most CADx systems aim to optimize classifiers for overall accuracy without considering the relative distribution of each …
Proposes a new data augmentation method for imbalanced datasets in both classification and regression.
A new algorithm enhances minority class representation in imbalanced datasets.
The paper explores how deep generative models can improve performance on imbalanced datasets.
Generative Adversarial Network model for class-imbalanced tabular data.
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…
Cardiovascular diseases are one of the most common causes of death in the world. Prevention, knowledge of previous cases in the family, and early detection is the best strategy to reduce this fact. Different machine learning approaches to automatic diagnostic are being proposed to this task. As in most health problems,…
LDAO addresses imbalanced regression by learning local distribution structures.
Framework learns to transform majority to minority samples for balanced classification.
Forums play an important role in providing a platform for community interaction. The introduction of irrelevant content or spam by individuals for commercial and social gains tends to degrade the professional experience presented to the forum users. Automated moderation of the relevancy of posted content is desired. Ma…
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…
Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods. One efficient strategy to deal with this problem is to employ resampling techniques before training the classifier. Existing multilabel sampling methods alleviate the (global) im…
GAN improves financial risk prediction by generating synthetic minority events.
Recently, the introduction of the generative adversarial network (GAN) and its variants has enabled the generation of realistic synthetic samples, which has been used for enlarging training sets. Previous work primarily focused on data augmentation for semi-supervised and supervised tasks. In this paper, we instead foc…
robROSE tackles imbalanced fraud data by creating synthetic samples and detecting outliers.
Class imbalanced datasets are common in real-world applications that range from credit card fraud detection to rare disease diagnostics. Several popular classification algorithms assume that classes are approximately balanced, and hence build the accompanying objective function to maximize an overall accuracy rate. In …
A novel two-stage resampling method improves CNN training on imbalanced colorectal cancer image data.
This paper surveys various data balancing methods for imbalanced datasets.
Study improves detection of cryptocurrency pump-and-dump schemes.
Proposes a new undersampling method for imbalanced data classification.
A novel resampling technique addresses class imbalance in imbalanced datasets.
A new method for generating synthetic time series improves forecasting model accuracy.
k-Nearest Neighbors is one of the most fundamental but effective classification models. In this paper, we propose two families of models built on a sequence to sequence model and a memory network model to mimic the k-Nearest Neighbors model, which generate a sequence of labels, a sequence of out-of-sample feature vecto…
CopulaSMOTE addresses class imbalance in diabetes prediction models.
The paper addresses classification imbalance by framing it as a transfer learning problem.
Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem, methods which generate artificial data to achieve a balanced cla…
A new oversampling framework generates minority samples by perturbing majority classes.
We improve kernel ridge regression for skewed responses using oversampling and adaptive partitioning.
Generative Adversarial Networks (GANs) have been used in many different applications to generate realistic synthetic data. We introduce a novel GAN with Autoencoder (GAN-AE) architecture to generate synthetic samples for variable length, multi-feature sequence datasets. In this model, we develop a GAN architecture with…
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…
EmDT generates synthetic fraud data to improve detection accuracy.