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Feature reduction is an important concept which is used for reducingdimensions to decrease the computation complexity and time of classification.Since now many approaches have been proposed for solving this problem, butalmost all of them just presented a fix output for each input dataset that someof them aren't satisfied cases for classification. In this we proposed anapproach as processing input dataset to increase accuracy rate of each featureextraction methods. First of all, a new concept called dispelling classesgradually (DCG) is proposed to increase separability of classes based on theirlabels. Next, this method is used to process input dataset of the featurereduction approaches to decrease the misclassification error rate of theiroutputs more than when output is achieved without any processing. In additionour method has a good quality to collate with noise based on adapting datasetwith feature reduction approaches. In the result part, two conditions (Withprocess and without that) are compared to support our idea by using some of UCIdatasets.

Dispelling Classes Gradually to Improve Quality of Feature Reduction Approaches