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imbalanced classification

learning classifier from class-imbalanced data

Papers

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Showing 126–150 of 211 papers

TitleStatusHype
The Unreasonable Effectiveness of the Final Batch Normalization Layer—0
Under-bagging Nearest Neighbors for Imbalanced Classification—0
Exploring Classification Equilibrium in Long-Tailed Object DetectionCode1
Equity-Directed Bootstrapping: Examples and Analysis—0
Deep Differential Amplifier for Extractive Summarization—0
Deep Metric Learning Model for Imbalanced Fault Diagnosis—0
A Linkage-based Doubly Imbalanced Graph Learning Framework for Face ClusteringCode0
MSE Loss with Outlying Label for Imbalanced Classification—0
Optimal Binary Classification Beyond AccuracyCode0
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization—0
DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised LearningCode1
White Paper Assistance: A Step Forward Beyond the Shortcut Learning—0
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness MetricsCode1
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph NetworksCode1
Zero Initialised Unsupervised Active Learning by Optimally Balanced Entropy-Based Sampling for Imbalanced ProblemsCode0
GMOTE: Gaussian based minority oversampling technique for imbalanced classification adapting tail probability of outliers—0
A Minimax Probability Machine for Non-Decomposable Performance Measures—0
MalNet: A Large-Scale Image Database of Malicious SoftwareCode1
Provably Training Overparameterized Neural Network Classifiers with Non-convex Constraints—0
Adaptive Threshold for Online Object Recognition and Re-identification TasksCode1
Attentional-Biased Stochastic Gradient DescentCode1
ReMix: Calibrated Resampling for Class Imbalance in Deep learning—0
Binary Classification: Counterbalancing Class Imbalance by Applying Regression Models in Combination with One-Sided Label Shifts—0
A Large-Scale Database for Graph Representation LearningCode1
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study—0
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