SOTAVerified

Multi-class Classification

Multi-class classification is a type of supervised learning where the goal is to assign an input to one of three or more distinct classes. Unlike binary classification (which has only two classes), multi-class classification handles multiple labels and uses algorithms like logistic regression, decision trees, random forests, SVMs, or neural networks to predict the correct category based on the features of the input data.

Papers

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Showing 326–350 of 903 papers

TitleStatusHype
Upper bounds on the Natarajan dimensions of some function classes—0
Semantic Interactive Learning for Text Classification: A Constructive Approach for Contextual InteractionsCode0
Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing LabelsCode0
CPS Attack Detection under Limited Local Information in Cyber Security: A Multi-node Multi-class Classification Ensemble Approach—0
Apple Counting using Convolutional Neural Networks—0
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning—0
Semi-Supervised Anomaly Detection Based on Quadratic Multiform Separation—0
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability—0
TagRec++: Hierarchical Label Aware Attention Network for Question CategorizationCode0
CIGAN: A Python Package for Handling Class Imbalance using Generative Adversarial NetworksCode1
Retrieval of surgical phase transitions using reinforcement learning—0
Factorizable Joint Shift in Multinomial Classification—0
Detecting Spam Reviews on Vietnamese E-commerce WebsitesCode1
A novel Deep Learning approach for one-step Conformal Prediction approximationCode0
MAPIE: an open-source library for distribution-free uncertainty quantificationCode3
Deep Sequence Models for Text Classification Tasks—0
Package for Fast ABC-BoostCode1
Learning Mutual Fund Categorization using Natural Language Processing—0
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent ApplicationsCode1
Multi-class Classification from Multiple Unlabeled Datasets with Partial Risk RegularizationCode0
University of Bucharest Team at Semeval-2022 Task4: Detection and Classification of Patronizing and Condescending Language—0
JBNU-CCLab at SemEval-2022 Task 7: DeBERTa for Identifying Plausible Clarifications in Instructional Texts—0
Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion ImagesCode0
Inductive Conformal Prediction: A Straightforward Introduction with Examples in PythonCode1
SFace: Privacy-friendly and Accurate Face Recognition using Synthetic DataCode1
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All datasetsCOVID-19 CXR DatasetCOVID chest X-rayReuters-52TII-SSRC-23Training and validation dataset of capsule vision 2024 challenge.

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COVID-CXNetAccuracy (%)94.2—Unverified
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1COVID-ResNetF1 score0.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified