Papers Multi-class Classification
“Multi-class Classification” 태그가 달린 논문 1,009편 · 필터 해제
FlexSED: Towards Open-Vocabulary Sound Event Detection
Despite recent progress in large-scale sound event detection (SED) systems capable of handling hundreds of sound classes, existing multi-class classification frameworks remain fundamentally limited. They cannot process f…
Multi-class ClassificationSound Event DetectionMental Multi-class Classification on Social Media: Benchmarking Transformer Architectures against LSTM Models
Millions of people openly share mental health struggles on social media, providing rich data for early detection of conditions such as depression, bipolar disorder, etc. However, most prior Natural Language Processing (N…
Multi-class ClassificationOn Optimal Steering to Achieve Exact Fairness
To fix the 'bias in, bias out' problem in fair machine learning, it is important to steer feature distributions of data or internal representations of Large Language Models (LLMs) to ideal ones that guarantee group-fair …
Multi-class ClassificationOptimal Learning from Label Proportions with General Loss Functions
Motivated by problems in online advertising, we address the task of Learning from Label Proportions (LLP). We introduce a novel and versatile low-variance debiasing methodology to learn from aggregate label information, …
Multi-class ClassificationCough Classification using Few-Shot Learning
This paper investigates the effectiveness of few-shot learning for respiratory sound classification, focusing on coughbased detection of COVID-19, Flu, and healthy conditions. We leverage Prototypical Networks with spect…
Multi-class ClassificationBinary ClassificationFew-Shot LearningM-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models
For Relation Extraction (RE), the manual annotation of training data may be prohibitively expensive, since the sentences that contain the target relations in texts can be very scarce and difficult to find. It is therefor…
Multi-class ClassificationBinary ClassificationRelation ExtractionContrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs
Network intrusion detection remains a critical challenge in cybersecurity. While supervised machine learning models achieve state-of-the-art performance, their reliance on large labelled datasets makes them impractical f…
Network Intrusion DetectionMulti-class ClassificationSelf-Supervised LearningBinary ClassificationRisk-averse Fair Multi-class Classification
We develop a new classification framework based on the theory of coherent risk measures and systemic risk. The proposed approach is suitable for multi-class problems when the data is noisy, scarce (relative to the dimens…
Multi-class ClassificationHandling imbalance and few-sample size in ML based Onion disease classification
Accurate classification of pests and diseases plays a vital role in precision agriculture, enabling efficient identification, targeted interventions, and preventing their further spread. However, current methods primaril…
Multi-class ClassificationBinary ClassificationData AugmentationART: Adaptive Resampling-based Training for Imbalanced Classification
Traditional resampling methods for handling class imbalance typically uses fixed distributions, undersampling the majority or oversampling the minority. These static strategies ignore changes in class-wise learning diffi…
Multi-class ClassificationConformalized Exceptional Model Mining: Telling Where Your Model Performs (Not) Well
Understanding the nuanced performance of machine learning models is essential for responsible deployment, especially in high-stakes domains like healthcare and finance. This paper introduces a novel framework, Conformali…
Multi-class ClassificationTowards Scalable Lottery Ticket Networks using Genetic Algorithms
Building modern deep learning systems that are not just effective but also efficient requires rethinking established paradigms for model training and neural architecture design. Instead of adapting highly overparameteriz…
Multi-class ClassificationModel CompressionActivity Coefficient-based Channel Selection for Electroencephalogram: A Task-Independent Approach
Electroencephalogram (EEG) signals have gained widespread adoption in brain-computer interface (BCI) applications due to their non-invasive, low-cost, and relatively simple acquisition process. The demand for higher spat…
Multi-class ClassificationProteoKnight: Convolution-based phage virion protein classification and uncertainty analysis
\textbf{Introduction:} Accurate prediction of Phage Virion Proteins (PVP) is essential for genomic studies due to their crucial role as structural elements in bacteriophages. Computational tools, particularly machine lea…
Multi-class ClassificationBinary ClassificationA Cooperative Game-Based Multi-Criteria Weighted Ensemble Approach for Multi-Class Classification
Since the Fourth Industrial Revolution, AI technology has been widely used in many fields, but there are several limitations that need to be overcome, including overfitting/underfitting, class imbalance, and the limitati…
Multi-class ClassificationEnsemble LearningNeural Network-Based Detection and Multi-Class Classification of FDI Attacks in Smart Grid Home Energy Systems
False Data Injection Attacks (FDIAs) pose a significant threat to smart grid infrastructures, particularly Home Area Networks (HANs), where real-time monitoring and control are highly adopted. Owing to the comparatively …
Multi-class ClassificationUnderstanding the Essence: Delving into Annotator Prototype Learning for Multi-Class Annotation Aggregation
Multi-class classification annotations have significantly advanced AI applications, with truth inference serving as a critical technique for aggregating noisy and biased annotations. Existing state-of-the-art methods typ…
Multi-class ClassificationHybrid activation functions for deep neural networks: S3 and S4 -- a novel approach to gradient flow optimization
Activation functions are critical components in deep neural networks, directly influencing gradient flow, training stability, and model performance. Traditional functions like ReLU suffer from dead neuron problems, while…
Multi-class ClassificationBinary ClassificationGNN-ASE: Graph-Based Anomaly Detection and Severity Estimation in Three-Phase Induction Machines
The diagnosis of induction machines has traditionally relied on model-based methods that require the development of complex dynamic models, making them difficult to implement and computationally expensive. To overcome th…
Multi-class ClassificationAnomaly DetectionFault DiagnosisImproving Diagnostic Accuracy of Pigmented Skin Lesions With CNNs: an Application on the DermaMNIST Dataset
Pigmented skin lesions represent localized areas of increased melanin and can indicate serious conditions like melanoma, a major contributor to skin cancer mortality. The MedMNIST v2 dataset, inspired by MNIST, was recen…
Multi-class ClassificationTransfer Learning