%0 Journal Article
%A Berthold K. P. Horn
%A CUI Hong-yan
%A Roy E. Welsch
%A XU Shuai
%A ZHANG Li-feng
%T The Key Techniques and Future Vision of Feature Selection in Machine Learning
%D 2018
%R 10.13190/j.jbupt.2017-150
%J Journal of Beijing University of Posts and Telecommunications
%P 1-12
%V 41
%N 1
%X Big data research is widely spread around the world, and feature selection of machine learning plays an important role on these researches. To address the issue of discovering novel feature selection methods in data mining tasks on big data, this paper researches five models related to feature selection:linear coefficient correlation, Lasso sparse selection, ensemble learning models, neural networks, principal component analysis. The merits and drawbacks of these models are extensively discussed in depth in this paper, which may help in providing a direction for those who are interested in the machine learning area.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2017-150