Open AccessEffect of Feature Selection on Gene Expression Datasets Classification Accurac
Author(s) -
Hicham Omara,
Mohamed Lazaar,
Youness Tabii
Publication year - 2018
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v8i5.pp3194-3203
Subject(s) - feature selection , artificial intelligence , classifier (uml) , computer science , pattern recognition (psychology) , selection (genetic algorithm) , machine learning , feature (linguistics) , data mining , philosophy , linguistics
Feature selection attracts researchers who deal with machine learning and data mining. It consists of selecting the variables that have the greatest impact on the dataset classification, and discarding the rest. This dimentionality reduction allows classifiers to be fast and more accurate. This paper traits the effect of feature selection on the accuracy of widely used classifiers in literature. These classifiers are compared with three real datasets which are pre-processed with feature selection methods. More than 9% amelioration in classification accuracy is observed, and k-means appears to be the most sensitive classifier to feature selection.