Dispelling Classes Gradually to Improve Quality of Feature Reduction Approaches

TL;DR

This paper introduces a novel preprocessing technique called dispelling classes gradually (DCG) that enhances class separability, thereby improving the accuracy and noise robustness of feature reduction methods in classification tasks.

Contribution

The paper proposes the DCG approach to preprocess datasets, increasing class separability and reducing misclassification errors across various feature reduction methods.

Findings

DCG improves classification accuracy over unprocessed data.

The method enhances noise robustness in feature reduction.

Experimental results on UCI datasets support the effectiveness of DCG.

Abstract

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.