期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2018
卷号：8
期号：5
页码：3194-3203
DOI：10.11591/ijece.v8i5.pp3194-3203
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：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.
其他摘要：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.
