@article{619,
  author = {Hamid Parvin, Zahra Rezaei, Alizadeh Hosein, Sajad Parvin},
  title = {An Extension of a Novel Classifier Ensemble Method Based on Class Weightening in Huge Dataset},
  journal = {Journal of Intelligent Computing},
  year = {2011},
  volume = {2},
  number = {3},
  doi = {},
  url = {http://www.dline.info/jic/fulltext/v2n3/3.pdf},
  abstract = {Many methods have been proposed for combining multiple classifiers in pattern recognition such as Random Forest which uses decision trees for problem solving. In this paper, we propose a weighted vote-based classifier ensemble method. The proposed method is similar to Random Forest method in employing many decision trees and neural networks as classifiers. For evaluating the proposed weighting method, both cases of decision tree and neural network classifiers are applied in experimental results. Main presumption of this method is that the reliability of the prediction of each classifier differs among classes. The proposed ensemble method is tested on a huge Persian data set of handwritten digits and shows improvement in comparison with competitors.},
}