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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 676–700 of 903 papers

TitleStatusHype
A One-Sided Classification Toolkit with Applications in the Analysis of Spectroscopy Data—0
Apple Counting using Convolutional Neural Networks—0
A pragmatic approach to multi-class classification—0
A priori estimates for classification problems using neural networks—0
A procedure for assessing of machine health index data prediction quality—0
A Rate Distortion Approach for Semi-Supervised Conditional Random Fields—0
A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography—0
ARGUABLY at ComMA@ICON: Detection of Multilingual Aggressive, Gender Biased, and Communally Charged Tweets Using Ensemble and Fine-Tuned IndicBERT—0
Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster—0
Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients—0
A scalable stage-wise approach to large-margin multi-class loss based boosting—0
A simple technique for improving multi-class classification with neural networks—0
Aspect category learning and sentimental analysis using weakly supervised learning—0
A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem—0
A Survey on Open Set Recognition—0
ATESA-BÆRT: A Heterogeneous Ensemble Learning Model for Aspect-Based Sentiment Analysis—0
Attention-based Region of Interest (ROI) Detection for Speech Emotion Recognition—0
A Tutorial on the Pretrain-Finetune Paradigm for Natural Language Processing—0
Respiratory Disease Classification and Biometric Analysis Using Biosignals from Digital Stethoscopes—0
A Universal Growth Rate for Learning with Smooth Surrogate Losses—0
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference—0
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification—0
Automated Fact-Checking of Claims in Argumentative Parliamentary Debates—0
Automated Multi-Label Classification based on ML-Plan—0
Automatic Classification of Functional Gait Disorders—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