Gene-expression-based cancer classification through feature selection with KNN and SVM classifiers

Author

Bouazza, Sara Haddou ; Hamdi, Nezha ; Zeroual, Abdelouhab ; Auhmani, Khalid

Author_Institution

Dept. of Phys., Cadi Ayyad Univ., Marrakech, Morocco

fYear

2015

fDate

25-26 March 2015

Firstpage

1

Lastpage

6

Abstract

This paper presents a study of feature selection methods effect, using a filter approach, on the accuracy and error of supervised classification of cancer. A comparative evaluation between different selection methods: Fisher, T-Statistics, SNR and ReliefF, is carried out, using the dataset of different cancers; leukemia cancer, prostate cancer and colon cancer. The classification results using k nearest neighbors (KNN) and support vector machine (SVM) classifiers show that the combination between SNR´s method and the SVM classifier can present the highest accuracy.

Keywords

cancer; feature selection; genetics; medical computing; pattern classification; support vector machines; KNN classifier; SNR method; SVM classifier; feature selection method; filter approach; gene expression-based cancer classification; k nearest neighbor; supervised classification; support vector machine; Accuracy; Colon; DNA; Prostate cancer; Signal to noise ratio; Support vector machines; DNA microarrays; KNN; SVM; colon cancer; feature selection; leukemia cancer; normalisation; prostate cancer; supervised classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems and Computer Vision (ISCV), 2015

Conference_Location

Fez

Print_ISBN

978-1-4799-7510-5

Type

conf

DOI

10.1109/ISACV.2015.7106168

Filename

7106168