Multi-class Classification
234 papers with code • 4 benchmarks • 12 datasets
Benchmarks
These leaderboards are used to track progress in Multi-class Classification
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Libraries
Use these libraries to find Multi-class Classification models and implementationsDatasets
CARER
SMM4H
CVE
SF-MASK
TII-SSRC-23
Most implemented papers
Exploiting Class Activation Value for Partial-Label Learning
As the first contribution, we empirically show that the class activation map (CAM), a simple technique for discriminating the learning patterns of each class in images, is surprisingly better at making accurate predictions than the model itself on selecting the true label from candidate labels.
MAPIE: an open-source library for distribution-free uncertainty quantification
scikit-learn-contrib/mapie
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Estimating uncertainties associated with the predictions of Machine Learning (ML) models is of crucial importance to assess their robustness and predictive power.
Inverse-Category-Frequency based supervised term weighting scheme for text categorization
Term weighting schemes often dominate the performance of many classifiers, such as kNN, centroid-based classifier and SVMs.
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks
It is not straightforward to integrate the content of each node in the current state-of-the-art network embedding methods.
Machine Learning Methods for Track Classification in the AT-TPC
ATTPC/event-classification
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We evaluate machine learning methods for event classification in the Active-Target Time Projection Chamber detector at the National Superconducting Cyclotron Laboratory (NSCL) at Michigan State University.
Enhanced Network Embedding with Text Information
TENE learns the representations of nodes under the guidance of both proximity matrix which captures the network structure and text cluster membership matrix derived from clustering for text information.
AMF: Aggregated Mondrian Forests for Online Learning
Using a variant of the Context Tree Weighting algorithm, we show that it is possible to efficiently perform an exact aggregation over all prunings of the trees; in particular, this enables to obtain a truly online parameter-free algorithm which is competitive with the optimal pruning of the Mondrian tree, and thus adaptive to the unknown regularity of the regression function.
Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice
By using MCSD as a measure of domain distance, we develop a new domain adaptation bound for multi-class UDA; its data-dependent, probably approximately correct bound is also developed that naturally suggests adversarial learning objectives to align conditional feature distributions across source and target domains.
Imbalance Learning for Variable Star Classification
In this work, we attempt to further improve hierarchical classification performance by applying 'data-level' approaches to directly augment the training data so that they better describe under-represented classes.
Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection
Shaul1321/nullspace_projection
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ACL 2020
The ability to control for the kinds of information encoded in neural representation has a variety of use cases, especially in light of the challenge of interpreting these models.
