机器学习中的特征选择方法研究及展望

The Key Techniques and Future Vision of Feature Selection in Machine Learning

任何领域的大数据研究都离不开用机器学习方法提取特征.为了探求满足海量大数据分析需求的特征选择方法,笔者对利用机器学习进行特征选择的常用方法做了深入分析,归纳总结出特征选择的五大类方法：相关性度量方法、Lasso稀疏选择方法、集成方法、神经网络方法、主成分分析方法.通过对比不同特征选择方法的原理、实现过程以及应用场景,给出了不同算法下进行特征选择时的适用范围、优缺点和关键点,为研究者提供参考.

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.