Semi-Supervised Online Elastic Extreme Learning Machine with Forgetting Parameter to deal with concept drift in data streams

2019 ◽  
Author(s):  
Carlos A. S. da Silva ◽  
Renato A. Krohling
Keyword(s):  
Extreme Learning Machine ◽  
Data Streams ◽  
Concept Drift ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document