Feature selection based on the training set manipulation

@article{Krzek2006FeatureSB,
  title={Feature selection based on the training set manipulation},
  author={Pavel Kr{\'i}zek and Josef Kittler and V{\'a}clav Hlav{\'a}c},
  journal={18th International Conference on Pattern Recognition (ICPR'06)},
  year={2006},
  volume={2},
  pages={658-661}
}
A novel filter feature selection technique is introduced. The method exploits the information conveyed by the evolution of the training samples weights similarly to the Adaboost algorithm. Features are selected on the basis of their individual merit using a simple error function. The weights dynamics and its effect on the error function are utilised to identify and remove redundant and irrelevant features. In experiments we show that the performance of commonly employed learning algorithms… CONTINUE READING

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