Dispelling Classes Gradually to Improve Quality of Feature Reduction
  Approaches
  Feature reduction is an important concept which is used for reducing
dimensions to decrease the computation complexity and time of classification.
Since now many approaches have been proposed for solving this problem, but
almost all of them just presented a fix output for each input dataset that some
of them aren't satisfied cases for classification. In this we proposed an
approach as processing input dataset to increase accuracy rate of each feature
extraction methods. First of all, a new concept called dispelling classes
gradually (DCG) is proposed to increase separability of classes based on their
labels. Next, this method is used to process input dataset of the feature
reduction approaches to decrease the misclassification error rate of their
outputs more than when output is achieved without any processing. In addition
our method has a good quality to collate with noise based on adapting dataset
with feature reduction approaches. In the result part, two conditions (With
process and without that) are compared to support our idea by using some of UCI
datasets.
