Papers imbalanced classification
“imbalanced classification” 태그가 달린 논문 211편 · 필터 해제
LSH-DynED: A Dynamic Ensemble Framework with LSH-Based Undersampling for Evolving Multi-Class Imbalanced Classification
The classification of imbalanced data streams, which have unequal class distributions, is a key difficulty in machine learning, especially when dealing with multiple classes. While binary imbalanced data stream classific…
imbalanced classificationCopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction
Diabetes mellitus poses a significant health risk, as nearly 1 in 9 people are affected by it. Early detection can significantly lower this risk. Despite significant advancements in machine learning for identifying diabe…
Data AugmentationDiabetes Predictionimbalanced classificationAsymptotic Normality of Infinite Centered Random Forests -Application to Imbalanced Classification
Many classification tasks involve imbalanced data, in which a class is largely underrepresented. Several techniques consists in creating a rebalanced dataset on which a classifier is trained. In this paper, we study theo…
imbalanced classificationvalidBridging Econometrics and AI: VaR Estimation via Reinforcement Learning and GARCH Models
In an environment of increasingly volatile financial markets, the accurate estimation of risk remains a major challenge. Traditional econometric models, such as GARCH and its variants, are based on assumptions that are o…
Deep Reinforcement LearningEconometricsimbalanced classificationKernel-Based Enhanced Oversampling Method for Imbalanced Classification
This paper introduces a novel oversampling technique designed to improve classification performance on imbalanced datasets. The proposed method enhances the traditional SMOTE algorithm by incorporating convex combination…
Classificationimbalanced classificationQCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation
The biomedical field is beginning to explore the use of quantum machine learning (QML) for tasks traditionally handled by classical machine learning, especially in predicting ADME (absorption, distribution, metabolism, a…
Classificationimbalanced classificationProperty PredictionQuantum Machine Learning+1Multi-Class Imbalanced Learning with Support Vector Machines via Differential Evolution
Support vector machine (SVM) is a powerful machine learning algorithm to handle classification tasks. However, the classical SVM is developed for binary problems with the assumption of balanced datasets. Obviously, the m…
imbalanced classificationGraph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin
Graph neural networks are gaining attention in fifth-generation (5G) core network digital twins, which are data-driven complex systems with numerous components. Analyzing these data can be challenging due to rare failure…
Graph ClassificationGraph Neural NetworkGraph Representation Learningimbalanced classification+1A statistical theory of overfitting for imbalanced classification
Classification with imbalanced data is a common challenge in data analysis, where certain classes (minority classes) account for a small fraction of the training data compared with other classes (majority classes). Class…
Classificationimbalanced classificationA Mathematics Framework of Artificial Shifted Population Risk and Its Further Understanding Related to Consistency Regularization
Data augmentation is an important technique in training deep neural networks as it enhances their ability to generalize and remain robust. While data augmentation is commonly used to expand the sample size and act as a c…
Data Augmentationimbalanced classificationBalancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data
Class imbalance remains a major challenge in machine learning, especially in multi-class problems with long-tailed distributions. Existing methods, such as data resampling, cost-sensitive techniques, and logistic loss mo…
imbalanced classificationDeep Learning Meets Oversampling: A Learning Framework to Handle Imbalanced Classification
Despite extensive research spanning several decades, class imbalance is still considered a profound difficulty for both machine learning and deep learning models. While data oversampling is the foremost technique to addr…
imbalanced classificationA binary PSO based ensemble under-sampling model for rebalancing imbalanced training data
Ensemble technique and under-sampling technique are both effective tools used for imbalanced dataset classification problems. In this paper, a novel ensemble method combining the advantages of both ensemble learning for …
Ensemble Learningimbalanced classificationTransfer Neyman-Pearson Algorithm for Outlier Detection
We consider the problem of transfer learning in outlier detection where target abnormal data is rare. While transfer learning has been considered extensively in traditional balanced classification, the problem of transfe…
Classificationimbalanced classificationOutlier DetectionTransfer LearningSynthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class
Handling imbalance in class distribution when building a classifier over tabular data has been a problem of long-standing interest. One popular approach is augmenting the training dataset with synthetically generated dat…
imbalanced classificationTabular Data GenerationGAT-RWOS: Graph Attention-Guided Random Walk Oversampling for Imbalanced Data Classification
Class imbalance poses a significant challenge in machine learning (ML), often leading to biased models favouring the majority class. In this paper, we propose GAT-RWOS, a novel graph-based oversampling method that combin…
Graph Attentionimbalanced classificationKernel-Free Universum Quadratic Surface Twin Support Vector Machines for Imbalanced Data
Binary classification tasks with imbalanced classes pose significant challenges in machine learning. Traditional classifiers often struggle to accurately capture the characteristics of the minority class, resulting in bi…
Binary ClassificationClassificationimbalanced classificationDist Loss: Enhancing Regression in Few-Shot Region through Distribution Distance Constraint
Imbalanced data distributions are prevalent in real-world scenarios, posing significant challenges in both imbalanced classification and imbalanced regression tasks. They often cause deep learning models to overfit in ar…
Deep Learningimbalanced classificationAn Oversampling-enhanced Multi-class Imbalanced Classification Framework for Patient Health Status Prediction Using Patient-reported Outcomes
Patient-reported outcomes (PROs) directly collected from cancer patients being treated with radiation therapy play a vital role in assisting clinicians in counseling patients regarding likely toxicities. Precise predicti…
imbalanced classificationZipfian Whitening
The word embedding space in neural models is skewed, and correcting this can improve task performance. We point out that most approaches for modeling, correcting, and measuring the symmetry of an embedding space implicit…
imbalanced classificationWord Embeddings