LAUSR

Gradient boosted trees for evolving data streams

Sign Up to like & get
recommendations!
0 Published in 2024 at "Machine Learning"

DOI: 10.1007/s10994-024-06517-y

Abstract: Gradient Boosting is a widely-used machine learning technique that has proven highly effective in batch learning. However, its effectiveness in stream learning contexts lags behind bagging-based ensemble methods, which currently dominate the field. One reason… read more here.

Keywords: boosted trees; evolving data; gradient; trees evolving ... See more keywords

Uterine transplantation: evolving data, success, and clinical importance.

Sign Up to like & get
recommendations!
0 Published in 2020 at "Journal of minimally invasive gynecology"

DOI: 10.1016/j.jmig.2020.12.015

Abstract: OBJECTIVE To review the literature to date in the field of uterine transplant. DATA SOURCES Peer reviewed published data. METHODS OF STUDY SELECTION A comprehensive pubmed literature search was performed using the terms "uterus transplant"… read more here.

Keywords: uterine transplant; transplant; uterine transplantation; evolving data ... See more keywords

A Systematic Review of Density Grid-Based Clustering for Data Streams

Sign Up to like & get
recommendations!
2 Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2021.3134704

Abstract: Various applications, such as electronic business, satellite remote sensing, intrusion discovery, and network traffic monitoring, generate large unbounded data stream sequences at a rapid pace. The clustering of data streams has attracted considerable interest due… read more here.

Keywords: based clustering; grid based; density grid; evolving data ... See more keywords

Federated Learning on Multilabel Evolving Data Streams

Sign Up to like & get
recommendations!
0 Published in 2025 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2025.3592954

Abstract: Multilabel classification in distributed evolving data stream environment presents significant challenges, including addressing distributed concept drifts and label dependencies. In this study, we introduce two novel solutions employing federated learning (FL) problem transformation techniques to… read more here.

Keywords: learning multilabel; evolving data; multilabel; federated learning ... See more keywords

Adversarial RL-Based IDS for Evolving Data Environment in 6LoWPAN

Sign Up to like & get
recommendations!
2 Published in 2022 at "IEEE Transactions on Information Forensics and Security"

DOI: 10.1109/tifs.2022.3214099

Abstract: Low-power and Lossy Networks (LLNs) comprise nodes characterised by constrained computational power, memory, and energy resources. The LLN nodes empower ubiquitous connections amongst numerous devices (e.g. temperature, humidity, and turbidity sensors, together with motors, valves… read more here.

Keywords: data environment; based ids; environment; adversarial based ... See more keywords