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 301–350 of 903 papers

TitleStatusHype
DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks—0
Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systems—0
Feature-aware conditional GAN for category text generation—0
Predicting delays in Indian lower courts using AutoML and Decision ForestsCode0
ATESA-BÆRT: A Heterogeneous Ensemble Learning Model for Aspect-Based Sentiment Analysis—0
Characterizing Data Point Vulnerability via Average-Case RobustnessCode0
Joint speech and overlap detection: a benchmark over multiple audio setup and speech domains—0
Eye Disease Classification Using Deep Learning Techniques—0
Mood Classification of Bangla Songs Based on Lyrics—0
Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review—0
Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation—0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
Improving Primate Sounds Classification using Binary Presorting for Deep Learning—0
Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?—0
Kalman Filter for Online Classification of Non-Stationary Data—0
A Generalized Unbiased Risk Estimator for Learning with Augmented ClassesCode0
Implicit Regularization in Feedback Alignment Learning Mechanisms for Neural Networks—0
A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem—0
A generalized framework to predict continuous scores from medical ordinal labelsCode0
Learning across Data Owners with Joint Differential Privacy—0
TaxoKnow: Taxonomy as Prior Knowledge in the Loss Function of Multi-class Classification—0
Solar Active Region Magnetogram Image Dataset for Studies of Space Weather—0
The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code ClassificationCode0
Venn Diagram Multi-label Class Interpretation of Diabetic Foot Ulcer with Color and Sharpness Enhancement—0
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression—0
T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer ClassificationCode0
Vision-based Estimation of Fatigue and Engagement in Cognitive Training SessionsCode0
Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus ImagesCode0
Performance of GAN-based augmentation for deep learning COVID-19 image classificationCode0
MisRoBÆRTa: Transformers versus MisinformationCode0
1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization for the ICPHM 2023 Data Challenge—0
Learning Optimal Fair Scoring Systems for Multi-Class Classification—0
KeyDetect --Detection of anomalies and user based on Keystroke Dynamics—0
A BERT-based Unsupervised Grammatical Error Correction Framework—0
Neuro-symbolic Rule Learning in Real-world Classification TasksCode0
A Novel Multi-Stage Approach for Hierarchical Intrusion DetectionCode0
Automatic pain recognition from Blood Volume Pulse (BVP) signal using machine learning techniques—0
Transformer Models for Acute Brain Dysfunction Prediction—0
DPPMask: Masked Image Modeling with Determinantal Point Processes—0
Machine learning tools to improve nonlinear modeling parameters of RC columns—0
Graph-based Extreme Feature Selection for Multi-class Classification Tasks—0
FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning—0
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?—0
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker—0
Occupant's Behavior and Emotion Based Indoor Environment's Illumination Regulation—0
Optimal Transport for Change Detection on LiDAR Point CloudsCode0
Capsules as viewpoint learners for human pose estimation—0
Cut your Losses with Squentropy—0
Conformalized Semi-supervised Random Forest for Classification and Abnormality DetectionCode0
Classified as unknown: A novel Bayesian neural network—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