Kernel PCA and SVM-RFE based feature selection for classification of dengue microarray dataset

Elke Annisa Octaria, Titin Siswantining, Alhadi Bustamam, Devvi Sarwinda

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Classification of microarray data are a challenging task because it has many features (genes) and a few samples in gene expression data. Feature selection on microarray data is very important processing in the classification method. Feature selection can produce fewer features to improve classification accuracy in high dimensional data. In this research, we compare two methods, namely kernel principal component analysis (Kernel PCA) and support vector machine - recursive feature elimination (SVM-RFE). Both are suitable methods for the selection of features. Kernel PCA is an extension of PCA using techniques of kernel methods, which works better on complicated spatial structures of high dimensional features. While SVM-RFE is an algorithm to select genes according to their weights. In this paper, the data taken from the National Center Biotechnology Information (NCBI) for Dengue fever microarray dataset. We choose the Support Vector Machine Classifier to classify our binary classes (dengue or health). From the experimental results, Kernel PCA and SVM-RFE have similarity accuracy (ACC), area under the curve (AUC), precision and recall but for running time, Kernel PCA only requires a short computational time than SVM-RFE for classification dengue and healthy patients on a dataset. So, Kernel PCA method as a feature selection is very helpful in improving the accuracy of classification performance and reducing the time consumption for the dengue microarray dataset.

Original languageEnglish
Title of host publicationSymposium on Biomathematics 2019, SYMOMATH 2019
EditorsMochamad Apri, Vitalii Akimenko
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735420243
DOIs
Publication statusPublished - 22 Sep 2020
EventSymposium on Biomathematics 2019, SYMOMATH 2019 - Bali, Indonesia
Duration: 25 Aug 201928 Aug 2019

Publication series

NameAIP Conference Proceedings
Volume2264
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

ConferenceSymposium on Biomathematics 2019, SYMOMATH 2019
Country/TerritoryIndonesia
CityBali
Period25/08/1928/08/19

Keywords

  • Classification
  • Dengue microarray dataset
  • Feature selection
  • Kernel PCA
  • SVM-RFE

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