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 301–325 of 903 papers

TitleStatusHype
Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach—0
Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces—0
Building an Interpretable Recommender via Loss-Preserving Transformation—0
Enhancing Suicide Risk Detection on Social Media through Semi-Supervised Deep Label Smoothing—0
Bridging Social Media via Distant Supervision—0
Ensemble-based Adversarial Defense Using Diversified Distance Mapping—0
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media—0
A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data—0
Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction—0
Fast Training of Effective Multi-class Boosting Using Coordinate Descent Optimization—0
E-PixelHop: An Enhanced PixelHop Method for Object Classification—0
Error-Correcting Factorization—0
Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks—0
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression—0
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network—0
Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews—0
Event-Event Relation Extraction using Probabilistic Box Embedding—0
Feature Incay for Representation Regularization—0
Everyone Likes Shopping! Multi-class Product Categorization for e-Commerce—0
Eye Disease Classification Using Deep Learning Techniques—0
Anomaly Detection using Ensemble Classification and Evidence Theory—0
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification—0
Classification based on Topological Data Analysis—0
Factorizable Joint Shift in Multinomial 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
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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
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified