Volume 18, Issue 2 (10-2021)                   JSDP 2021, 18(2): 29-44 | Back to browse issues page

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Ghaffari H R, Jalali Mojahed A. Feature extraction based on the more resolution of the classes using auxiliary classifiers. JSDP. 2021; 18 (2) :29-44
URL: http://jsdp.rcisp.ac.ir/article-1-986-en.html
Department of Computer Engineering,Department of Computer Engineering, University of Ferdows, Ferdows, Iran
Abstract:   (589 Views)
Classification is a machine learning method used to predict a particular sample’s label with the least error. The present study was conducted using label prediction ability with the help of a classifier to create a new feature. Today, there are several feature-extraction methods like principal component analysis (PCA) and independent component analysis (ICA) that are widely used in different fields; however, they all suffer from the high cost of transferring to another space. The purpose of the proposed method was to create a higher distinction between various classes using the new feature in a way that, make the data in the classes closer to each other. As a result, for increasing the efficiency of classifiers, more differentiation is created between the data of various classes. Firstly, the suggested labels for the primary data set were determined using one or more classifiers and added to the primary data set as a new feature. The model was created using a new data set. The new feature for training and testing data sets was provided separately. The tests were performed on 20 standard data sets and the results of the proposed method were compared with those of the two methods described in the related studies. The outputs indicated that the proposed method has significantly improved the classification accuracy. In the second part of the tests, the resolution of the new feature was examined according to two criteria, namely Information Gain and Gini Index, for examining the effectiveness of the proposed method. The results showed that the feature obtained in the proposed method has higher Information Gain and lower Gini Index in most cases, as it has less irregularity. To prevent the increase in data dimensions, the feature with the least Information Gain was replaced with the feature extracted with the most Information Gain. The results of this step showed an increase in efficiency as well.
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Type of Study: Research | Subject: Paper
Received: 2019/03/14 | Accepted: 2020/08/18 | Published: 2021/10/8 | ePublished: 2021/10/8

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