Cover Image

Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Camacho, José, Wasielewska, Katarzyna, Bro, Rasmus, Kotz, David
Format:	Preprint
Published:	2019
Subjects:	Networking and Internet Architecture Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks
by: Singh, Kamal, et al.
Published: (2025)

TSKAN: Interpretable Machine Learning for QoE modeling over Time Series Data
by: Singh, Kamal, et al.
Published: (2025)

Active Learning for WBAN-based Health Monitoring
by: Chiu, Cho-Chun, et al.
Published: (2024)

Intelligent Monitoring Framework for Cloud Services: A Data-Driven Approach
by: Srinivas, Pooja, et al.
Published: (2024)

KISS: Keeping it Simple and Slotted when Learning to Communicate over Wireless
by: Szczech, Kamil, et al.
Published: (2026)

Adaptive Alarm Threshold Prediction in 4G Mobile Networks: A Percentile-Guided Deep Learning Framework with Interpretable Outputs
by: Roy, Ayon, et al.
Published: (2026)

ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction
by: Rong, Yi, et al.
Published: (2024)

A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data
by: Rezakhani, Mahshid, et al.
Published: (2025)

Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
by: Belkhiter, Yannis, et al.
Published: (2025)

Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speed
by: Yan, Jinzhu, et al.
Published: (2024)

Multivariate Wireless Link Quality Prediction Based on Pre-trained Large Language Models
by: Yan, Zhuangzhuang, et al.
Published: (2025)

Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning
by: Toure, Babacar, et al.
Published: (2026)

pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data
by: Ni, Zhou, et al.
Published: (2025)

Lightweight Intrusion Detection in IoT via SHAP-Guided Feature Pruning and Knowledge-Distilled Kronecker Networks
by: Benaddi, Hafsa, et al.
Published: (2025)

Diffusion Models as Network Optimizers: Explorations and Analysis
by: Liang, Ruihuai, et al.
Published: (2024)

Context-Aware Mobile Network Performance Prediction Using Network & Remote Sensing Data
by: Shibli, Ali, et al.
Published: (2024)

Optimistic Learning for Communication Networks
by: Iosifidis, George, et al.
Published: (2025)

Contrastive Learning for Correlating Network Incidents
by: Dötterl, Jeremias
Published: (2025)

Versatile yet Efficient Network Traffic Analysis: Offloading Network Foundation Model to SmartNIC
by: Lin, Chungang, et al.
Published: (2025)

Network Traffic Analysis with Process Mining: The UPSIDE Case Study
by: Vitale, Francesco, et al.
Published: (2025)

Causal Intervention Sequence Analysis for Fault Tracking in Radio Access Networks
by: Shi, Chenhua, et al.
Published: (2025)

Deep Learning Network-Temporal Models For Traffic Prediction
by: Xin, Yufeng, et al.
Published: (2026)

Distributed Learning and Inference Systems: A Networking Perspective
by: Moussa, Hesham G., et al.
Published: (2025)

Liquid Neural Network-based Adaptive Learning vs. Incremental Learning for Link Load Prediction amid Concept Drift due to Network Failures
by: Ayoub, Omran, et al.
Published: (2024)

Automatic Generation of Digital Twins for Network Testing
by: Ding, Shenjia, et al.
Published: (2025)

Network Calculus with Flow Prolongation -- A Feedforward FIFO Analysis enabled by ML
by: Geyer, Fabien, et al.
Published: (2022)

An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization
by: Manias, Dimitrios Michael, et al.
Published: (2022)

Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks
by: Zhang, Zifan, et al.
Published: (2024)

Reconstructing Fine-Grained Network Data using Autoencoder Architectures with Domain Knowledge Penalties
by: Cheung, Mark, et al.
Published: (2025)

Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach
by: Deng, Lei, et al.
Published: (2025)

On the Robustness of Deep Learning-predicted Contention Models for Network Calculus
by: Geyer, Fabien, et al.
Published: (2019)

PLATONT: Learning a Platonic Representation for Unified Network Tomography
by: Du, Chengze, et al.
Published: (2025)

Self-Explaining Reinforcement Learning for Mobile Network Resource Allocation
by: Nowosadko, Konrad, et al.
Published: (2025)

Mobility-Aware Federated Self-supervised Learning in Vehicular Network
by: Gu, Xueying, et al.
Published: (2024)

Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Review
by: Mahdimahalleh, Sajjad Emdadi
Published: (2023)

Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks
by: Huang, Yudi, et al.
Published: (2024)

Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Sampling
by: Geng, Jiaxiang, et al.
Published: (2024)

Representation Learning of Tangled Key-Value Sequence Data for Early Classification
by: Duan, Tao, et al.
Published: (2024)

Q-Learning-Based Time-Critical Data Aggregation Scheduling in IoT
by: Vo, Van-Vi, et al.
Published: (2025)

Inference-to-complete: A High-performance and Programmable Data-plane Co-processor for Neural-network-driven Traffic Analysis
by: Wen, Dong, et al.
Published: (2024)