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 226–250 of 903 papers

TitleStatusHype
Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframing—0
Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning—0
ThangDLU at #SMM4H 2024: Encoder-decoder models for classifying text data on social disorders in children and adolescents—0
Critical Review for One-class Classification: recent advances and the reality behind them—0
LM-IGTD: a 2D image generator for low-dimensional and mixed-type tabular data to leverage the potential of convolutional neural networks—0
Interval Abstractions for Robust Counterfactual ExplanationsCode0
Multiclass ROC—0
Multi-Class Quantum Convolutional Neural Networks—0
Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification—0
Top-k Classification and Cardinality-Aware Prediction—0
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning—0
Large Language Models for Multi-Choice Question Classification of Medical Subjects—0
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis—0
FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network—0
Neural Network Learning and Quantum Gravity—0
A Tutorial on the Pretrain-Finetune Paradigm for Natural Language Processing—0
HemaGraph: Breaking Barriers in Hematologic Single Cell Classification with Graph AttentionCode0
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry BenchmarkingCode0
Multi-class Temporal Logic Neural Networks—0
Understanding Self-Distillation and Partial Label Learning in Multi-Class Classification with Label Noise—0
A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification—0
PowerGraph: A power grid benchmark dataset for graph neural networks—0
Leveraging Human-Machine Interactions for Computer Vision Dataset Quality EnhancementCode0
Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case StudyCode0
Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning—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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified