Study on Ensemble Classification Methods towards Spam Filtering

2009 ◽  
pp. 314-325 ◽  
Author(s):  
Jinlong Wang ◽  
Ke Gao ◽  
Yang Jiao ◽  
Gang Li
Keyword(s):  
Ensemble Classification ◽  
Classification Methods ◽  
Spam Filtering

Using ensemble classification methods in lung cancer disease*

2019 ◽  
Author(s):  
Mohamed Hosni ◽  
Juan M. Carrillo-de-Gea ◽  
Ali Idri ◽  
Jose Luis Fernandez-Aleman ◽  
Jose A. Garcia-Berna
Keyword(s):  
Lung Cancer ◽  
Ensemble Classification ◽  
Classification Methods ◽  
Cancer Disease

scholarly journals Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 158 ◽  
Author(s):  
Yange Sun ◽  
Han Shao ◽  
Shasha Wang
Keyword(s):  
Real World ◽  
Data Streams ◽  
Concept Drift ◽  
Classification Performance ◽  
Ensemble Classification ◽  
Classification Methods ◽  
Stream Data ◽  
Label Data ◽  
Real World Datasets ◽  
Jensen Shannon Divergence

Most existing multi-label data streams classification methods focus on extending single-label streams classification approaches to multi-label cases, without considering the special characteristics of multi-label stream data, such as label dependency, concept drift, and recurrent concepts. Motivated by these challenges, we devise an efficient ensemble paradigm for multi-label data streams classification. The algorithm deploys a novel change detection based on Jensen–Shannon divergence to identify different kinds of concept drift in data streams. Moreover, our method tries to consider label dependency by pruning away infrequent label combinations to enhance classification performance. Empirical results on both synthetic and real-world datasets have demonstrated its effectiveness.

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