Classification Code
Classification code research focuses on developing and improving algorithms and models to accurately assign data points to predefined categories. Current efforts concentrate on addressing challenges like imbalanced datasets, noisy data, and limited labeled data through techniques such as self-supervised pre-training, robust loss functions, and the application of diverse architectures including convolutional neural networks (CNNs), transformers, and novel approaches like Mamba. These advancements have significant implications across various fields, improving accuracy and efficiency in applications ranging from medical image analysis and bioacoustic monitoring to cybersecurity threat detection and scientific literature organization.
Papers
Classification of Deceased Patients from Non-Deceased Patients using Random Forest and Support Vector Machine Classifiers
Dheeman Saha, Aaron Segura, Biraj Tiwari
Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data
Aoran Shen, Minghao Dai, Jiacheng Hu, Yingbin Liang, Shiru Wang, Junliang Du
Advancements in Myocardial Infarction Detection and Classification Using Wearable Devices: A Comprehensive Review
Abhijith S, Arjun Rajesh, Mansi Manoj, Sandra Davis Kollannur, Sujitta R V, Jerrin Thomas Panachakel
Machine learning-based classification for Single Photon Space Debris Light Curves
Nadine M. Trummer, Amit Reza, Michael A. Steindorfer, Christiane Helling
Causal and Local Correlations Based Network for Multivariate Time Series Classification
Mingsen Du, Yanxuan Wei, Xiangwei Zheng, Cun Ji
Vision Mamba Distillation for Low-resolution Fine-grained Image Classification
Yao Chen, Jiabao Wang, Peichao Wang, Rui Zhang, Yang Li
Leveraging Large Language Models and Topic Modeling for Toxicity Classification
Haniyeh Ehsani Oskouie, Christina Chance, Claire Huang, Margaret Capetz, Elizabeth Eyeson, Majid Sarrafzadeh
Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification
Yuanchen Bei, Weizhi Chen, Hao Chen, Sheng Zhou, Carl Yang, Jiapei Fan, Longtao Huang, Jiajun Bu
A Novel Data Augmentation Tool for Enhancing Machine Learning Classification: A New Application of the Higher Order Dynamic Mode Decomposition for Improved Cardiac Disease Identification
Nourelhouda Groun, Maria Villalba-Orero, Lucia Casado-Martin, Enrique Lara-Pezzi, Eusebio Valero, Jesus Garicano-Mena, Soledad Le Clainche
Context-Aware Detection of Mixed Critical Events using Video Classification
Filza Akhlaq, Alina Arshad, Muhammad Yehya Hayati, Jawwad A. Shamsi, Muhammad Burhan Khan
GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification
Tianqi Zhao, Megha Khosla
FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles
Yijun Zhai, Pengzhan Zhou, Yuepeng He, Fang Qu, Zhida Qin, Xianlong Jiao, Guiyan Liu, Songtao Guo
A Multimodal Approach to The Detection and Classification of Skin Diseases
Allen Yang (1), Edward Yang (2), ((1) Mission San Jose High School, Fremont, CA, (2) Yale University, New Haven, CT)
Hierarchical Text Classification (HTC) vs. eXtreme Multilabel Classification (XML): Two Sides of the Same Medal
Nerijus Bertalis, Paul Granse, Ferhat Gül, Florian Hauss, Leon Menkel, David Schüler, Tom Speier, Lukas Galke, Ansgar Scherp
An Evolutional Neural Network Framework for Classification of Microarray Data
Maryam Eshraghi Evari, Md Nasir Sulaiman, Amir Rajabi Behjat
Combining Autoregressive and Autoencoder Language Models for Text Classification
João Gonçalves