A review of improved extreme learning machine methods for data stream classification

2019 ◽  
Vol 78 (23) ◽  
pp. 33375-33400 ◽  
Author(s):  
Li Li ◽  
Ruizhi Sun ◽  
Saihua Cai ◽  
Kaiyi Zhao ◽  
Qianqian Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Data Stream ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Learning Machine

scholarly journals An Ensemble Extreme Learning Machine for Data Stream Classification

Algorithms ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 107 ◽  
Author(s):  
Rui Yang ◽  
Shuliang Xu ◽  
Lin Feng
Keyword(s):  
Extreme Learning Machine ◽  
Data Stream ◽  
Learning Strategy ◽  
Concept Drift ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Classification Tasks ◽  
Learning Machine ◽  
Hidden Layer ◽  
Short Time

Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN). Because ELM has a fast speed for classification, it is widely applied in data stream classification tasks. In this paper, a new ensemble extreme learning machine is presented. Different from traditional ELM methods, a concept drift detection method is embedded; it uses online sequence learning strategy to handle gradual concept drift and uses updating classifier to deal with abrupt concept drift, so both gradual concept drift and abrupt concept drift can be detected in this paper. The experimental results showed the new ELM algorithm not only can improve the accuracy of classification result, but also can adapt to new concept in a short time.

scholarly journals Intrusion Detection System Using Data Stream Classification

2021 ◽  
pp. 319-328
Author(s):  
Amer Abdulmajeed Abdualrahman ◽  
Mahmood Khalel Ibrahem
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Data Stream ◽  
Detection System ◽  
Data Communication ◽  
Computation Time ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Network Intrusion ◽  
Learning Machine

Secure data communication across networks is always threatened with intrusion and abuse. Network Intrusion Detection System (IDS) is a valuable tool for in-depth defense of computer networks. Most research and applications in the field of intrusion detection systems was built based on analysing the several datasets that contain the attacks types using the classification of batch learning machine. The present study presents the intrusion detection system based on Data Stream Classification. Several data stream algorithms were applied on CICIDS2017 datasets which contain several new types of attacks. The results were evaluated to choose the best algorithm that satisfies high accuracy and low computation time.

Analyzing and repairing concept drift adaptation in data stream classification

2021 ◽  
Author(s):  
Ben Halstead ◽  
Yun Sing Koh ◽  
Patricia Riddle ◽  
Russel Pears ◽  
Mykola Pechenizkiy ◽  
...  
Keyword(s):  
Data Stream ◽  
Concept Drift ◽  
Stream Classification ◽  
Data Stream Classification
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