scholarly journals GPU-Accelerated Extreme Learning Machines for Imbalanced Data Streams with Concept Drift

2016 ◽  
Vol 80 ◽  
pp. 1692-1701 ◽  
Author(s):  
Bartosz Krawczyk
Keyword(s):  
Data Streams ◽  
Concept Drift ◽  
Imbalanced Data ◽  
Extreme Learning Machines ◽  
Learning Machines

Ensemble Learning via Extreme Learning Machines for Imbalanced Data

2020 ◽  
pp. 59-88
Author(s):  
Adnan Omer Abuassba ◽  
Dezheng O. Zhang ◽  
Xiong Luo
Keyword(s):  
Ensemble Learning ◽  
Imbalanced Data ◽  
Noisy Data ◽  
Data Classification ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Learning Machine ◽  
Heterogeneous Ensemble

Ensembles are known to reduce the risk of selecting the wrong model by aggregating all candidate models. Ensembles are known to be more accurate than single models. Accuracy has been identified as an important factor in explaining the success of ensembles. Several techniques have been proposed to improve ensemble accuracy. But, until now, no perfect one has been proposed. The focus of this research is on how to create accurate ensemble learning machine (ELM) in the context of classification to deal with supervised data, noisy data, imbalanced data, and semi-supervised data. To deal with mentioned issues, the authors propose a heterogeneous ELM ensemble. The proposed heterogeneous ensemble of ELMs (AELME) for classification has different ELM algorithms, including regularized ELM (RELM) and kernel ELM (KELM). The authors propose new diverse AdaBoost ensemble-based ELM (AELME) for binary and multiclass data classification to deal with the imbalanced data issue.

scholarly journals A comprehensive active learning method for multiclass imbalanced data streams with concept drift

2021 ◽  
Vol 215 ◽  
pp. 106778
Author(s):  
Weike Liu ◽  
Hang Zhang ◽  
Zhaoyun Ding ◽  
Qingbao Liu ◽  
Cheng Zhu
Keyword(s):  
Active Learning ◽  
Data Streams ◽  
Concept Drift ◽  
Imbalanced Data ◽  
Learning Method ◽  
Active Learning Method

scholarly journals VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams

2021 ◽  
Author(s):  
Alessio Bernardo ◽  
Emanuele Della Valle
Keyword(s):  
Data Streams ◽  
Concept Drift ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Real Data ◽  
Minority Class ◽  
Machine Learning Classification ◽  
Imbalance Learning ◽  
Class Imbalance Learning ◽  
Better Than

AbstractThe world is constantly changing, and so are the massive amount of data produced. However, only a few studies deal with online class imbalance learning that combines the challenges of class-imbalanced data streams and concept drift. In this paper, we propose the very fast continuous synthetic minority oversampling technique (VFC-SMOTE). It is a novel meta-strategy to be prepended to any streaming machine learning classification algorithm aiming at oversampling the minority class using a new version of Smote and Borderline-Smote inspired by Data Sketching. We benchmarked VFC-SMOTE pipelines on synthetic and real data streams containing different concept drifts, imbalance levels, and class distributions. We bring statistical evidence that VFC-SMOTE pipelines learn models whose minority class performances are better than state-of-the-art. Moreover, we analyze the time/memory consumption and the concept drift recovery speed.

The Gradual Resampling Ensemble for mining imbalanced data streams with concept drift

2018 ◽  
Vol 286 ◽  
pp. 150-166 ◽  
Author(s):  
Siqi Ren ◽  
Bo Liao ◽  
Wen Zhu ◽  
Zeng Li ◽  
Wei Liu ◽  
...  
Keyword(s):  
Data Streams ◽  
Concept Drift ◽  
Imbalanced Data
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