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.
We improve kernel ridge regression for skewed responses using oversampling and adaptive partitioning.
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…
Proposes GMOTE for better handling imbalanced data.
LDAO addresses imbalanced regression by learning local distribution structures.
This paper surveys various data balancing methods for 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…
This paper tackles imbalanced data in binary classification problems.
INGB improves oversampling for noisy imbalanced datasets.
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.
This paper tackles imbalanced classification with weakly supervised oversampling.
Proposes a new technique for handling imbalanced data.
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…
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…
Paper tackles imbalanced time series classification with a novel oversampling method.
GAT-RWOS uses graph attention to improve imbalanced data classification.
The paper explores how deep generative models can improve performance on imbalanced datasets.
ART adapts class-wise resampling to improve imbalanced classification performance.
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…
This paper uses LLMs to generate synthetic data to improve classification accuracy in imbalanced datasets.
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…
This chapter tackles class imbalance in datasets to promote data democracy.
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…
Proposes a new sampling method for online learning with cumulative oversampling.
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…
A new LLM-based method enhances diversity in oversampling for imbalanced classification.
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,…
The paper analyzes SMOTE for imbalanced classification, providing theoretical bounds and guidelines.
Data imbalance remains one of the most widespread problems affecting contemporary machine learning. The negative effect data imbalance can have on the traditional learning algorithms is most severe in combination with other dataset difficulty factors, such as small disjuncts, presence of outliers and insufficient numbe…
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…
QPCA improves PCA for cyclostationary data.
In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very lim…
Paper tackles gene mutation prediction for HCC using multi-instance multi-label learning.
A novel resampling technique addresses class imbalance in imbalanced datasets.
A new algorithm enhances minority class representation in imbalanced datasets.
The OSTSC package is a powerful oversampling approach for classifying univariant, but multinomial time series data in R. This article provides a brief overview of the oversampling methodology implemented by the package. A tutorial of the OSTSC package is provided. We begin by providing three test cases for the user to …
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.
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…
Proposes a new undersampling method for imbalanced data classification.
Dealing with imbalanced data is one of the main challenges in machine/deep learning algorithms for classification. This issue is more important with log message data as it is typically very imbalanced and negative logs are rare. In this paper, a model is proposed to generate text log messages using a SeqGAN network. Th…
A new algorithm reduces imbalanced data classification errors in multi-class settings.
The paper addresses classification imbalance by framing it as a transfer learning problem.
Proposes a new data augmentation method for imbalanced datasets in both classification and regression.
Framework learns to transform majority to minority samples for balanced classification.
OS-ELM outperforms other classifiers in software defect prediction.
Machine learning fails to improve recession prediction with yield spread.
RSmote improves PINNs accuracy with less memory usage.