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 451–500 of 903 papers

TitleStatusHype
Overview of the Fifth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2020—0
Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021—0
PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification—0
Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors—0
Performance-Guided LLM Knowledge Distillation for Efficient Text Classification at Scale—0
Performance Improvement in Multi-class Classification via Automated Hierarchy Generation and Exploitation through Extended LCPN Schemes—0
Personalized Federated Learning with Exact Stochastic Gradient Descent—0
Phrase-level Self-Attention Networks for Universal Sentence Encoding—0
Pitfalls of Assessing Extracted Hierarchies for Multi-Class Classification—0
PMP-Swin: Multi-Scale Patch Message Passing Swin Transformer for Retinal Disease Classification—0
Polyphonic audio event detection: multi-label or multi-class multi-task classification problem?—0
Pool-Based Active Learning with Proper Topological Regions—0
Pose Estimation Based on 3D Models—0
Post Selection Inference with Kernels—0
PowerGraph: A power grid benchmark dataset for graph neural networks—0
Powerset multi-class cross entropy loss for neural speaker diarization—0
PRACH Preamble Detection as a Multi-Class Classification Problem: A Machine Learning Approach Using SVM—0
Predicting Cascading Failures in Power Systems using Machine Learning—0
Predicting Customer Call Intent by Analyzing Phone Call Transcripts based on CNN for Multi-Class Classification—0
Predicting Loss Risks for B2B Tendering Processes—0
Prediction and outlier detection in classification problems—0
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms—0
Privacy-Preserving Model and Preprocessing Verification for Machine Learning—0
Probabilistic Classification Vector Machine for Multi-Class Classification—0
Probabilistic Quantum SVM Training on Ising Machine—0
Prognostic classification based on random convolutional kernel—0
Progressive Fashion Attribute Extraction—0
Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classification—0
Provably Consistent Partial-Label Learning—0
Punctuation as Native Language Interference—0
QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest—0
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates—0
Quantum Complex-Valued Self-Attention Model—0
Quantum neural networks facilitating quantum state classification—0
Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses—0
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding—0
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding—0
Question Relatedness on Stack Overflow: The Task, Dataset, and Corpus-inspired Models—0
QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers—0
Random Forests for Big Data—0
Randomized Kernel Methods for Least-Squares Support Vector Machines—0
Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks—0
Region-based Discriminative Feature Pooling for Scene Text Recognition—0
Regression under demographic parity constraints via unlabeled post-processing—0
Regularized Co-Clustering with Dual Supervision—0
Relational Similarity Machines—0
ReportAGE: Automatically extracting the exact age of Twitter users based on self-reports in tweets—0
Representative Functional Connectivity Learning for Multiple Clinical groups in Alzheimer's Disease—0
Disease2Vec: Representing Alzheimer's Progression via Disease Embedding Tree—0
Residual Generation Using Physically-Based Grey-Box Recurrent Neural Networks For Engine Fault Diagnosis—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
#ModelMetricClaimedVerifiedStatus
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