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 651–700 of 903 papers

TitleStatusHype
Minimizing Supervision in Multi-label Categorization—0
Model agnostic local variable importance for locally dependent relationships—0
Model-Agnostic Private Learning via Stability—0
Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings—0
Model Transfer with Explicit Knowledge of the Relation between Class Definitions—0
Mood Classification of Bangla Songs Based on Lyrics—0
MulBot: Unsupervised Bot Detection Based on Multivariate Time Series—0
Multi-borders classification—0
Multi-channel deep convolutional neural networks for multi-classifying thyroid disease—0
Anomaly Detection for Scenario-based Insider Activities using CGAN Augmented Data—0
Multi-Class Classification from Noisy-Similarity-Labeled Data—0
Multi-Class Classification from Single-Class Data with Confidences—0
Multi-class classification: mirror descent approach—0
Multi-class Classification Model Inspired by Quantum Detection Theory—0
Multi-Class Classification of Blood Cells -- End to End Computer Vision based diagnosis case study—0
Multi-Class classification of vulnerabilities in Smart Contracts using AWD-LSTM, with pre-trained encoder inspired from natural language processing—0
Multi-Class Deep Boosting—0
Multi-class Generative Adversarial Nets for Semi-supervised Image Classification—0
Multi-class granular approximation by means of disjoint and adjacent fuzzy granules—0
Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data—0
Multiclass Language Identification using Deep Learning on Spectral Images of Audio Signals—0
Multi-Class Learning: From Theory to Algorithm—0
Multi-Class Optimal Margin Distribution Machine—0
Multi-class Probabilistic Bounds for Self-learning—0
Multi-Class Quantum Convolutional Neural Networks—0
Multiclass ROC—0
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms—0
Multi-class Temporal Logic Neural Networks—0
Multi Expression Programming for solving classification problems—0
Multi-function Convolutional Neural Networks for Improving Image Classification Performance—0
Multi-Label Adversarial Perturbations—0
Multi-Label Classification Method Based on Extreme Learning Machines—0
Multi-label Contrastive Predictive Coding—0
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators—0
Multi-layer Domain Adaptation for Deep Convolutional Networks—0
Multi-level Activation for Segmentation of Hierarchically-nested Classes—0
Multi-loss ensemble deep learning for chest X-ray classification—0
Multimodal Depression Severity Prediction from medical bio-markers using Machine Learning Tools and Technologies—0
Multiobjective Optimization of Classifiers by Means of 3-D Convex Hull Based Evolutionary Algorithm—0
Multi-output Classification for Compound Fault Diagnosis in Motor under Partially Labeled Target Domain—0
Multi-output Classification Framework and Frequency Layer Normalization for Compound Fault Diagnosis in Motor—0
Multi-output Classification using a Cross-talk Architecture for Compound Fault Diagnosis of Motors in Partially Labeled Condition—0
Multi-Output Distributional Fairness via Post-Processing—0
Multi-Participant Multi-Class Vertical Federated Learning—0
Multiple birth least squares support vector machine for multi-class classification—0
Multiple birth support vector machine for multi-class classification—0
Multi-resolution Annotations for Emoji Prediction—0
Multi-Scale Dual-Branch Fully Convolutional Network for Hand Parsing—0
Multi-Task Determinantal Point Processes for Recommendation—0
Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text—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