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 351–400 of 903 papers

TitleStatusHype
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification—0
Conformal inference is (almost) free for neural networks trained with early stoppingCode0
Increasing Fairness via Combination with Learning Guarantees—0
ComplAI: Theory of A Unified Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models—0
Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification—0
Anomaly Detection using Ensemble Classification and Evidence Theory—0
Learning Disentangled Label Representations for Multi-label Classification—0
Semi-supervised binary classification with latent distance learning—0
X-PuDu at SemEval-2022 Task 7: A Replaced Token Detection Task Pre-trained Model with Pattern-aware Ensembling for Identifying Plausible Clarifications—0
Condensed Gradient BoostingCode0
A Masked Face Classification Benchmark on Low-Resolution Surveillance ImagesCode0
Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder—0
Okapi: Generalising Better by Making Statistical Matches MatchCode0
Generalized Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary LossesCode0
Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classification—0
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem—0
An Attention-based Long Short-Term Memory Framework for Detection of Bitcoin Scams—0
Proximal Mean Field Learning in Shallow Neural NetworksCode0
An Effective Approach for Multi-label Classification with Missing Labels—0
Calibration tests beyond classificationCode0
Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals—0
Systematic Evaluation of Predictive FairnessCode0
Transformer-Based Speech Synthesizer Attribution in an Open Set Scenario—0
Generalization Analysis on Learning with a Concurrent Verifier—0
Effective Metaheuristic Based Classifiers for Multiclass Intrusion Detection—0
MultiGuard: Provably Robust Multi-label Classification against Adversarial ExamplesCode0
Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data—0
UB Health Miners@SMM4H’22: Exploring Pre-processing Techniques To Classify Tweets Using Transformer Based Pipelines.—0
Is Encoder-Decoder Transformer the Shiny Hammer?—0
Class-Imbalanced Complementary-Label Learning via Weighted Loss—0
Source detection via multi-label classificationCode0
CAMRI Loss: Improving Recall of a Specific Class without Sacrificing AccuracyCode0
MulBot: Unsupervised Bot Detection Based on Multivariate Time Series—0
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian ManifoldCode0
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
Retrieval of surgical phase transitions using reinforcement learning—0
Factorizable Joint Shift in Multinomial Classification—0
A novel Deep Learning approach for one-step Conformal Prediction approximationCode0
Deep Sequence Models for Text Classification Tasks—0
Learning Mutual Fund Categorization using Natural Language Processing—0
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
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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
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1SVM (tficf)Macro F173.9—Unverified
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1Extra TreesF1-Score93.36—Unverified
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1Multi-Model EnsembleMean AUC0.99—Unverified