To increase quality of feature reduction approaches based on processing input datasets
Shervan Fekri-Ershad, Sattar Hashemi · 2011
Feature extraction is an important concept which is used for reducing features to decrease the complexity and time of classification. So far some methods have been presented 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 paper first we present a new concept called Dispelling Classes Gradually (DCG) to increase separability of classes based on their labels. Next we will use this method 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. The results compare two conditions (With process and without that) to support our idea.