Title : 
Improving classification through ensemble neural networks
         
        
            Author : 
Zaamout, Khobaib ; Zhang, John Z.
         
        
            Author_Institution : 
Dept. of Math. & Comput. Sci., Univ. of Lethbridge, Lethbridge, AB, Canada
         
        
        
        
        
        
            Abstract : 
We consider using neural networks as an ensemble technique to improve classification accuracy. Neural networks are among the best techniques used for classification. In this work, we make use of ensemble approach to combine individual neural networks´ outputs by another neural network. Furthermore, we propose to include original data as additional inputs for the ensemble neural network. The effectiveness of our proposed approach is demonstrated through a series of experiments on real and synthetic datasets.
         
        
            Keywords : 
neural nets; pattern classification; classification accuracy; ensemble neural network; original data; real dataset; synthetic dataset; Accuracy; Artificial neural networks; Biological neural networks; Digital signal processing; Principal component analysis; Training; Neural networks; classification; ensemble neural networks;
         
        
        
        
            Conference_Titel : 
Natural Computation (ICNC), 2012 Eighth International Conference on
         
        
            Conference_Location : 
Chongqing
         
        
        
            Print_ISBN : 
978-1-4577-2130-4
         
        
        
            DOI : 
10.1109/ICNC.2012.6234540