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 401–450 of 903 papers

TitleStatusHype
Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach—0
Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals—0
Hierarchical Feature Hashing for Fast Dimensionality Reduction—0
Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data—0
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach—0
Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification—0
How can we generalise learning distributed representations of graphs?—0
How many faces can be recognized? Performance extrapolation for multi-class classification—0
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?—0
Convolutional Neural Networks in Multi-Class Classification of Medical Data—0
Hybrid Machine Learning Model for Detecting Bangla Smishing Text Using BERT and Character-Level CNN—0
EC3: Combining Clustering and Classification for Ensemble Learning—0
Identifying Domain Independent Update Intents in Task Based Dialogs—0
Image Classification using Combination of Topological Features and Neural Networks—0
COV-ELM classifier: An Extreme Learning Machine based identification of COVID-19 using Chest X-Ray Images—0
Impact of Feature Selection on Micro-Text Classification—0
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem—0
Improved Generalization Bounds for Adversarially Robust Learning—0
Improving automated segmentation of radio shows with audio embeddings—0
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification—0
Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning—0
CPS Attack Detection under Limited Local Information in Cyber Security: A Multi-node Multi-class Classification Ensemble Approach—0
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling—0
Improving Primate Sounds Classification using Binary Presorting for Deep Learning—0
Improving the Accuracy of Learning Example Weights for Imbalance Classification—0
Cross-domain Recommendation via Deep Domain Adaptation—0
Biomedical Event Extraction by Multi-class Classification of Pairs of Text Entities—0
Revisiting Classification Perspective on Scene Text Recognition—0
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition—0
In-Context Learning for Label-Efficient Cancer Image Classification in Oncology—0
Increasing Fairness via Combination with Learning Guarantees—0
Incremental user embedding modeling for personalized text classification—0
Inducing a hierarchy for multi-class classification problems—0
ML-KFHE: Multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter—0
CyberLearning: Effectiveness Analysis of Machine Learning Security Modeling to Detect Cyber-Anomalies and Multi-Attacks—0
Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems—0
Data-dependent Generalization Bounds for Multi-class Classification—0
Insight: A Multi-Modal Diagnostic Pipeline using LLMs for Ocular Surface Disease Diagnosis—0
Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data—0
Dynamic Spectrum Matching with One-shot Learning—0
Integrating Deep Feature Extraction and Hybrid ResNet-DenseNet Model for Multi-Class Abnormality Detection in Endoscopic Images—0
Interpretable Rule-Based System for Radar-Based Gesture Sensing: Enhancing Transparency and Personalization in AI—0
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference—0
Introducing the DOME Activation Functions—0
Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset—0
Dynamic Sentence Boundary Detection for Simultaneous Translation—0
Biomarker based Cancer Classification using an Ensemble with Pre-trained Models—0
A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations—0
Investigating Self-Supervised Methods for Label-Efficient Learning—0
DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines—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