Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7220
Title: A comparison of feature selection algorithms for cancer classification through gene expression data: Leukemia case
Authors: Taşçı, Aslı
İnce, Türker
Güzeliş, Cüneyt
Keywords: Cancer classification
Gene expression
Diseases
Feature extraction
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Taşçı, A., İnce, T., and Güzeliş, C. (2017). A comparison of feature selection algorithms for cancer classification through gene expression data: Leukemia case. Paper presented at the 10th International Conference on Electrical and Electronics Engineering, ELECO 2017, Bursa, 2 December (pp.1352-1354).
Abstract: In this study, three different feature selection algorithms are compared using Support Vector Machines as classifier for cancer classification through gene expression data. The ability of feature selection algorithms to select an optimal gene subset for a cancer type is evaluated by the classification ability of selected genes. A publicly available micro array dataset is employed for gene expression values. Selected gene subsets were able to classify subtypes of the considered cancer type with high accuracies and showed that these feature selection methods were applicable for bio-marker gene selection.
Description: 10th International Conference on Electrical and Electronics Engineering, ELECO 2017; Bursa; Turkey; 29 November 2017 through 2 December 2017
URI: https://hdl.handle.net/11147/7220
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File Description SizeFormat 
7220.pdfConference Paper98.67 kBAdobe PDFThumbnail
View/Open
Show full item record

CORE Recommender

Page view(s)

60
checked on Jul 4, 2022

Download(s)

28
checked on Jul 4, 2022

Google ScholarTM

Check


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.