Classification of high-dimensional evolving data streams via a resource-efficient online ensemble

2017 ◽  
Vol 31 (5) ◽  
pp. 1242-1265 ◽  
Author(s):  
Tingting Zhai ◽  
Yang Gao ◽  
Hao Wang ◽  
Longbing Cao
Keyword(s):  
Data Streams ◽  
High Dimensional ◽  
Evolving Data

Learning High-Dimensional Evolving Data Streams With Limited Labels

2021 ◽  
pp. 1-12
Author(s):  
Salah Ud Din ◽  
Jay Kumar ◽  
Junming Shao ◽  
Cobbinah Bernard Mawuli ◽  
Waldiodio David Ndiaye
Keyword(s):  
Data Streams ◽  
High Dimensional ◽  
Evolving Data

A framework for on-demand classification of evolving data streams

2006 ◽  
Vol 18 (5) ◽  
pp. 577-589 ◽  
Author(s):  
C.C. Aggarwal ◽  
J. Han ◽  
Jianyong Wang ◽  
P.S. Yu
Keyword(s):  
Data Streams ◽  
On Demand ◽  
Evolving Data ◽  
Demand Classification

scholarly journals Classification of Evolving Data Streams with Infinitely Delayed Labels

2015 ◽  
Author(s):  
Vinicius M.A. Souza ◽  
Diego F. Silva ◽  
Gustavo E.A.P.A. Batista ◽  
Joao Gama
Keyword(s):  
Data Streams ◽  
Evolving Data

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

scholarly journals Adaptive online incremental learning for evolving data streams

2021 ◽  
Vol 105 ◽  
pp. 107255
Author(s):  
Si-si Zhang ◽  
Jian-wei Liu ◽  
Xin Zuo
Keyword(s):  
Data Streams ◽  
Incremental Learning ◽  
Evolving Data ◽  
Online Incremental Learning
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