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 551–575 of 903 papers

TitleStatusHype
Stable Learning in Coding Space for Multi-Class Decoding and Its Extension for Multi-Class Hypothesis Transfer Learning—0
Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning—0
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification—0
Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning—0
Streaming Network Embedding through Local Actions—0
StructBoost: Boosting Methods for Predicting Structured Output Variables—0
Student Performance Prediction with Optimum Multilabel Ensemble Model—0
Sub-Classifier Construction for Error Correcting Output Code Using Minimum Weight Perfect Matching—0
Supervised level-wise pretraining for recurrent neural network initialization in multi-class classification—0
Support matrix machine: A review—0
TabKAN: Advancing Tabular Data Analysis using Kolmogorov-Arnold Network—0
Take One Step at a Time to Know Incremental Utility of Demonstration: An Analysis on Reranking for Few-Shot In-Context Learning—0
Robust Adversarial Classification via Abstaining—0
TAPAS: Two-pass Approximate Adaptive Sampling for Softmax—0
Target Fishing: A Single-Label or Multi-Label Problem?—0
TaxoKnow: Taxonomy as Prior Knowledge in the Loss Function of Multi-class Classification—0
Tensor Decompositions for Modeling Inverse Dynamics—0
Tensor Valued Common and Individual Feature Extraction: Multi-dimensional Perspective—0
ThangDLU at #SMM4H 2024: Encoder-decoder models for classifying text data on social disorders in children and adolescents—0
The Best of Both Worlds: Combining Data-independent and Data-driven Approaches for Action Recognition—0
The efficacy of various machine learning models for multi-class classification of RNA-seq expression data—0
The Fourier Discrepancy Function—0
Theoretical Analysis of Adversarial Learning: A Minimax Approach—0
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention—0
The SAMME.C2 algorithm for severely imbalanced multi-class classification—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