The experimental results show that SLED has fewer false positives, higher precision, and higher Matthews correlation coefficient while maintaining reasonably ...

In each training session, the goal of the training is to learn a better set of weights and assign them to each base detector through a local weighting mechanism ...

They propose a semi-supervised classifier combined with an active learning approach based on. Bayesian stream data for online activity recognition. The proposed ...

SLED: Semi-supervised Locally-weighted Ensemble Detector · Shuxiang Zhang, David Tse Jung Huang, Gillian Dobbie, Yun Sing Koh. 2020 (modified: 11 Jun 2022) ...

Apr 25, 2024 · SLED: Semi-supervised Locally-weighted Ensemble Detector. ICDE 2020: 1838-1841. [c2]. view. electronic edition via DOI · unpaywalled version ...

Nov 28, 2022 · In this paper, an extension of the DyDaSL drift detection module is proposed. Its main aim is to make drift detection more flexible and, in turn, to improve ...

Digging into Computer Science Students' Learning Journals · SLED: Semi-supervised Locally-weighted Ensemble Detector · Exploring Topic Difficulty in Information ...

SLED: Semi-supervised locally-weighted ensemble detector. S Zhang, DTJ Huang, G Dobbie, YS Koh. 2020 IEEE 36th International Conference on Data Engineering ...

To address the challenges of online learning for multi-resident HAR, we propose a novel online learning architecture based on a locally-weighted ensemble ...

A semi-supervised locally-weighted ensemble detector (SLED), where the relative performance among its base detectors is characterized by a set of weights ...