@TechReport{Krizek-TR-2005-07,
  IS = { zkontrolovano 11 Nov 2005 },
  UPDATE  = { 2005-03-09 },
author =      {K{\v r}{\'\i }{\v z}ek, Pavel},
title =       {Feature selection based on the training set 
               manipulation -- {PhD} thesis proposal},
institution = {Center for Machine Perception,  K13133 FEE
               Czech Technical University},
address =     {Prague, Czech Republic},
year =        {2005},
month =       {February},
type =        {Research Report},
number =      {{CTU--CMP--2005--07}},
issn =        {1213-2365},
pages =       {32},
figures =     {13},
authorship =  {100},
psurl       = {[Krizek-TR-2005-07.pdf]},
project =     {GACR 102/03/0440},
annote = {A novel feature selection technique for the classification
  problems is proposed in this PhD thesis proposal. The method is
  based on the training set manipulation. A weight is associated with
  each training sample similarly as it is in the AdaBoost
  algorithm. The weights form a distribution. Any change of the
  distribution of weights influences the behaviour of particular
  features in a different manner. This brings new information to the
  selection process in contrast to other feature selection
  techniques. The main idea is to modify the weights in each selection
  step so that the currently selected feature appears, with respect to
  the distribution, like an irrelevant observation. We show in
  experiments that such a change of the weights distribution allows to
  reveal hidden relationships between features. Although the feature
  selection algorithm is not completely developed yet, preliminary
  results achieved on several artificial problem looks promising.},
keywords =    {Feature selection, AdaBoost},
}