TiWS-iForest: Isolation forest in weakly supervised and tiny ML scenarios-Reference-Cited by-同舟云学术

TiWS-iForest: Isolation forest in weakly supervised and tiny ML scenarios

Published:2022-09 Issue: Volume:610 Page:126-143
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Barbariol Tommaso,Susto Gian Antonio

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference32 articles.

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1. A Machine Learning-Oriented Survey on Tiny Machine Learning;IEEE Access;2024

2. An Intelligent LoRaWAN-Based IoT Device for Monitoring and Control Solutions in Smart Farming Through Anomaly Detection Integrated With Unsupervised Machine Learning;IEEE Access;2024

3. On-Device Tiny Machine Learning for Anomaly Detection Based on the Extreme Values Theory;IEEE Micro;2023-11

4. Minimal spanning tree-based isolation forest with anomaly score function built on the basis of fuzzy rules;Applied Soft Computing;2023-11

5. RoSAS: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision;Information Processing & Management;2023-09