Feature selection in machine learning: A new perspective

NEUROCOMPUTING

卷:300

Article

Cai, Jie¹  Luo, Jiawei¹  Wang, Shulin¹  Yang, Sheng¹ 

[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha, Hunan, Peoples R China

关键词: Feature selection;    Dimensionality reduction;    Machine learning;    Data mining;   

DOI  :  10.1016/j.neucom.2017.11.077

来源: Elsevier

【 摘 要 】

High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, and facilitate a better understanding for the learning model or data. In this study, we discuss several frequentlyused evaluation measures for feature selection, and then survey supervised, unsupervised, and semisupervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering. Lastly, future challenges about feature selection are discussed.

【 授权许可】

Free