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Youness Tabii
University Abdelmalak Essaadi
Computer Science & IT Electrical & Electronics Engineering

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Effect of Feature Selection on Gene Expression Datasets Classification Accurac Hicham Omara; Mohamed Lazaar; Youness Tabii
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (245.739 KB) | DOI: 10.11591/ijece.v8i5.pp3194-3203

Abstract

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.
Co-Authors Hicham Omara Mohamed Lazaar