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 476–500 of 903 papers

TitleStatusHype
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
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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