imbalanced classification
52 papers with code • 0 benchmarks • 6 datasets
learning classifier from class-imbalanced data
Benchmarks
These leaderboards are used to track progress in imbalanced classification
Datasets
CI-MNIST
WikiChurches
Industrial Benchmark Dataset for Customer Escalation Prediction
Most implemented papers
MoleculeNet: A Benchmark for Molecular Machine Learning
However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods.
Deep Reinforcement Learning for Imbalanced Classification
linenus/DRL-For-imbalanced-Classification
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The agent finally finds an optimal classification policy in imbalanced data under the guidance of specific reward function and beneficial learning environment.
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning
The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance.
Large-Scale Long-Tailed Recognition in an Open World
zhmiao/OpenLongTailRecognition-OLTR
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CVPR 2019
We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes.
MNIST-MIX: A Multi-language Handwritten Digit Recognition Dataset
In this letter, we contribute a multi-language handwritten digit recognition dataset named MNIST-MIX, which is the largest dataset of the same type in terms of both languages and data samples.
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts
frank-xwang/RIDE-LongTailRecognition
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ICLR 2021
We take a dynamic view of the training data and provide a principled model bias and variance analysis as the training data fluctuates: Existing long-tail classifiers invariably increase the model variance and the head-tail model bias gap remains large, due to more and larger confusion with hard negatives for the tail.
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
ZhiningLiu1998/mesa
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NeurIPS 2020
This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks.
A Large-Scale Database for Graph Representation Learning
With the rapid emergence of graph representation learning, the construction of new large-scale datasets is necessary to distinguish model capabilities and accurately assess the strengths and weaknesses of each technique.
Box Drawings for Learning with Imbalanced Data
The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes.
Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost Tuning
We then demonstrate how this insight can be used to attain a good compromise between the rare and abundant classes without having to resort to cost set tuning, which has long been the norm for imbalanced classification.
