摘要：Feature selection (FS) is an important research topic in the area of data mining and machine learning. FS aims at dealing with the high dimensionality problem. It is the process of selecting the relevant features and removing the irrelevant, redundant and noisy ones, intending to obtain the best performing subset of original features without any transformation. This paper provides a comprehensive review of FS literature intending to supplement insights and recommendations to help readers. Moreover, an empirical study of six well-known feature selection methods is presented so as to critically analyzing their applicability.
