Mendeley
TY  _  JOUR
ID  -  139408301151423
TI  -  A New Ensemble Learning Method for Improvement of Classification Performance
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Nabavi-Kerizi S. H.
AU  -  Kabir E.
AD  -  دانشگاه تربیت مدرس
AD  -  دانشگاه تربیت مدرس
Y1  -  2006
PY  -  2006
VL  -  2
IS  -  
SP  -  99
EP  -  104
KW  -  Classifiers combinationensemble learningdiversity creationnegative correlation learningneural networks
DO  -  
N2  -  The combination of multiple classifiers is shown to be suitable for improving the performance of pattern recognition systems. Combining multiple classifiers is only effective if the individual classifiers are accurate and diverse. The methods have been proposed for diversity creation can be classified into implicit and explicit methods. In this paper, we propose a new explicit method for diversity creation. Our method adds a new penalty term in learning algorithm of neural network ensembles. This term for each network is the product of its error and the sum of other networks errors. Experimental results on different data sets show that proposed method outperforms the independent training and the negative correlation learning methods.
UR  - rimag.ir/en/Article/27859
L1  - rimag.ir/en/Article/Download/27859
ER  -  
