Evaluation of Data Classification Methods Base on Random Forest, SVM before and after Using Feature Selection Method Guided Random Forest for High Dimensional Gene Data

Received: 15-02-2017

Accepted: 08-01-2018

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Issue: Vol. 15 No. 12 (2017)

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KỸ THUẬT VÀ CÔNG NGHỆ

How to Cite:

Ha, H. (2024). Evaluation of Data Classification Methods Base on Random Forest, SVM before and after Using Feature Selection Method Guided Random Forest for High Dimensional Gene Data. Vietnam Journal of Agricultural Sciences, 15(12), 1653–1663. http://testtapchi.vnua.edu.vn/index.php/vjasvn/article/view/414

Evaluation of Data Classification Methods Base on Random Forest, SVM before and after Using Feature Selection Method Guided Random Forest for High Dimensional Gene Data

Hoang Thi Ha (*) 1

  • 1 Khoa Công nghệ thông tin, Học viện Nông nghiệp Việt Nam

    • Keywords

    Classification, high dimensional data, machine learning, random forest, feature selection

    Abstract


    Data classification is a common method used for mining the potentialknowledge from large databases. Several methods have been proposed such as AdaBoost, Support Vector Machine (sVM), Neural Network, random forest (RF), C45… Among all these classifiers, Random forest and SVM provided more accurate and efficient result for high-dimensional data. However, the performance of these algorithms may be affected/degraded when working on gene data with thousands of features. This is because gene data usually contain many redundant features which are uninformative to classification. Therefore, using the subsets of selected genes may give a better performance than using all the features. In this study, we summarizedthe data classification algorithms based on the random forest models, SVM, and evaluate the effectiveness of the algorithms for classifying high-dimensional data. Next, we evaluatedthe combination the features selection using GRF and other classifiers such as RF, WSRF, RUF, SVM. Sevengene data sets were used to evaluate the methods. Experimental results showedthat the combination not only increasedthe accuracy but also decreasedthe execution time of the algorithms

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