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 801–825 of 903 papers

TitleStatusHype
Discriminative training for Convolved Multiple-Output Gaussian processes—0
Discriminative Training of Deep Fully-connected Continuous CRF with Task-specific Loss—0
Distribution-Free Federated Learning with Conformal Predictions—0
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift—0
Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation—0
Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification—0
Double-Stage Feature-Level Clustering-Based Mixture of Experts Framework—0
DPPMask: Masked Image Modeling with Determinantal Point Processes—0
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities—0
DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines—0
Dynamic Sentence Boundary Detection for Simultaneous Translation—0
Dynamic Spectrum Matching with One-shot Learning—0
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem—0
EC3: Combining Clustering and Classification for Ensemble Learning—0
Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach—0
Effective Metaheuristic Based Classifiers for Multiclass Intrusion Detection—0
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media—0
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification—0
Elimination of All Bad Local Minima in Deep Learning—0
Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis—0
EMG Signal Classification for Neuromuscular Disorders with Attention-Enhanced CNN—0
End-to-End Automatic Speech Recognition with Deep Mutual Learning—0
Energy-based features and bi-LSTM neural network for EEG-based music and voice classification—0
Energy-based Out-of-distribution Detection for Multi-label Classification—0
Enhanced H-Consistency Bounds—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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified