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 851–875 of 903 papers

TitleStatusHype
Interval Abstractions for Robust Counterfactual ExplanationsCode0
Adaptive Sampled Softmax with Inverted Multi-Index: Methods, Theory and ApplicationsCode0
A Masked Face Classification Benchmark on Low-Resolution Surveillance ImagesCode0
Deep attention-based classification network for robust depth predictionCode0
Inverse-Category-Frequency based supervised term weighting scheme for text categorizationCode0
Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language ProcessingCode0
Coarse and Fine-Grained Hostility Detection in Hindi Posts using Fine Tuned Multilingual EmbeddingsCode0
DECT-based Space-Squeeze Method for Multi-Class Classification of Metastatic Lymph Nodes in Breast CancerCode0
Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot LearningCode0
Predicting delays in Indian lower courts using AutoML and Decision ForestsCode0
Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection MethodCode0
CAMRI Loss: Improving Recall of a Specific Class without Sacrificing AccuracyCode0
Semantic Interactive Learning for Text Classification: A Constructive Approach for Contextual InteractionsCode0
Multimodal Speech Emotion Recognition and Ambiguity ResolutionCode0
A Generalized Unbiased Risk Estimator for Learning with Augmented ClassesCode0
Primal-Dual Block Frank-WolfeCode0
Primal-Dual Block Generalized Frank-WolfeCode0
SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERTCode0
Attention-based Context Aggregation Network for Monocular Depth EstimationCode0
A generalized framework to predict continuous scores from medical ordinal labelsCode0
Probabilistic Truly Unordered Rule SetsCode0
DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer LearningCode0
Truly Unordered Probabilistic Rule Sets for Multi-class ClassificationCode0
KréyoLID From Language Identification Towards Language MiningCode0
Semi-Supervised Deep Learning with MemoryCode0
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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
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1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified