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 276–300 of 903 papers

TitleStatusHype
Capsules as viewpoint learners for human pose estimation—0
A Novel Online Real-time Classifier for Multi-label Data Streams—0
A novel online multi-label classifier for high-speed streaming data applications—0
A Fully Memristive Spiking Neural Network with Unsupervised Learning—0
Aanisha@TamilNLP-ACL2022:Abusive Detection in Tamil—0
Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation—0
Enhanced H-Consistency Bounds—0
Candidates vs. Noises Estimation for Large Multi-Class Classification Problem—0
On the computational complexity of the probabilistic label tree algorithms—0
End-to-End Automatic Speech Recognition with Deep Mutual Learning—0
Explainable Multi-class Classification of Medical Data—0
Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—0
EMG Signal Classification for Neuromuscular Disorders with Attention-Enhanced CNN—0
Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis—0
A Novel Adaptive Hybrid Focal-Entropy Loss for Enhancing Diabetic Retinopathy Detection Using Convolutional Neural Networks—0
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification—0
Elimination of All Bad Local Minima in Deep Learning—0
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification—0
Building an Interpretable Recommender via Loss-Preserving Transformation—0
Bridging Social Media via Distant Supervision—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
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
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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